Arize Phoenix had its biggest year yet in 2024, with over 2.5 million monthly downloads of their open-source LLM evaluation and tracing solution, growing from ~20k to reach this milestone. The community expanded to over 6,000 members, and the team hosted numerous events, including hackathons, meetups, workshops, tech talks, and conferences. Notable themes in 2024 included the rapid growth of agents in the AI industry, with tools launching weekly but still facing challenges; OpenTelemetry solidifying its position as a preferred standard for LLM observability; and the maturity of LLM evaluations, with new features launched to help developers run their evals. The team is now poised to build the best possible AI platform, with no reservations, and is excited for big plans in 2025, including more features, community events, experiments, and releases.
This comprehensive survey on LLMs-as-Judges paradigm examines the framework across five dimensions: functionality, methodology, applications, meta-evaluation, and limitations. It discusses how LLMs as judges evaluate outputs or components of AI applications for quality, relevance, and accuracy, providing scores, rankings, categorical labels, explanations, and actionable feedback. These outputs enable users to refine AI applications iteratively, reducing dependency on human annotations using interpretable explanations. The survey breaks down the concept into five dimensions, including functionality, methodology, applications, meta-evaluation, and limitations, highlighting its advantages, limitations, and methods for evaluating its effectiveness. It also explores three main input types for evaluation: pointwise, pairwise, and listwise, as well as various criteria for assessment, such as linguistic quality, content accuracy, task-specific metrics, user experience, reference-based vs. reference-free evaluation, and applications across diverse fields like summarization, multimodal models, and domain-specific use cases. Despite their promise, LLM judges face notable challenges, including bias, domain expertise limitations, prompt sensitivity, adversarial vulnerabilities, and resource intensity. The paper suggests strategies to mitigate these limitations, such as regularly auditing for bias, incorporating domain experts, standardizing prompt designs, combining human oversight with automated evaluation systems, and aligning application-specific criteria with stakeholder goals. Overall, the survey underscores the transformative potential of LLMs as evaluators while emphasizing the importance of addressing their limitations, highlighting the need for robust, scalable evaluation frameworks in AI systems.
The Arize platform has released several new features and enhancements, including the Prompt Hub, a centralized repository for managing prompt templates, and managed code evaluators to simplify evaluation tasks. The Prompt Hub allows users to save and share templates, collaborate on projects, and evaluate template performance. Additionally, Arize has improved its experiment creation flow, added a new monitor visualization, and supported LangChain instrumentation with native thread tracking in TypeScript. These updates aim to enhance the platform's collaboration capabilities, streamline workflows, and facilitate more efficient use of large language models.
Booking.com has leveraged artificial intelligence to revolutionize trip planning with its AI Trip Planner, a tool that combines domain-specific optimizations, in-house fine-tuned LLMs, and real-time monitoring powered by Arize AI to deliver highly personalized travel recommendations. The AI Trip Planner integrates seamlessly into the user journey, driving improved accuracy, efficiency, and user satisfaction. To overcome challenges such as domain-specific limitations, high latency and costs, complexity in orchestration, and evaluation gaps, Booking.com implemented innovative solutions including a GenAI Orchestrator, Arize AI's Comprehensive Evaluation Framework, and fine-tuned LLMs using parameter-efficient fine-tuning techniques like LoRA and QLoRA. The AI Trip Planner provides personalized recommendations tailored to individual preferences, explainable results, and seamless funnel integration, making it a standout example of how AI can transform the travel experience.
Continuous Integration and Continuous Deployment (CI/CD) pipelines can be used to evaluate large language models (LLMs) effectively by integrating LLM evaluations into your CI/CD pipelines, ensuring consistent and reliable AI performance and automating experimental results from your AI applications. To set up a CI/CD pipeline for LLM evaluations, you need to create a dataset of test cases, define tasks that represent the work your system is doing, create evaluators to measure outputs, run experiments, and add a yml file to prepare your script as for CI/CD. Best practices include automating LLM evaluation in CI/CD pipelines, combining quantitative and qualitative evaluations, using version control for models, data, and CI/CD configurations, and leveraging tools like Arize Phoenix to improve reliability and observability.
The AI conferences of 2025 are shaping up to be exciting events that bring together industry leaders, researchers, and practitioners to discuss the latest advancements in Artificial Intelligence. The World AI Conference is one of the largest conferences, featuring over 12,000 attendees and top speakers from companies like IBM and Google. Other notable conferences include the NVIDIA GTC, Google Cloud Next, QCon, and the World Summit AI. There are also numerous regional and specialized conferences, such as the Data Science Salon, MLConf, and AI in Finance Summit, which cater to specific interests and industries. These events offer opportunities for networking, learning from experts, and staying up-to-date with the latest tools and strategies in AI. With a wide range of topics and formats, there's something for everyone at these conferences.
Researchers have developed collaborative strategies to address the diversity of large language models (LLMs), which often exhibit distinct strengths and weaknesses due to differences in their training corpora. The paper "Merge, Ensemble, and Cooperate" highlights three primary approaches: merging, ensemble, and cooperation. Merging involves integrating multiple LLMs into a single model, while ensemble strategies focus on combining their outputs to generate a high-quality result. Cooperation encompasses various techniques where LLMs collaborate to achieve specific objectives, leveraging their unique strengths. These collaborative strategies offer innovative ways to maximize the capabilities of LLMs, but real-world applications require balancing performance, cost, and latency.
Arize has released new updates, including enhancements to Copilot, Experiment Projects, and additional features. The Copilot Span Chat skill allows for faster analysis of span data, while the Dashboard Widget Generator simplifies building dashboard plots from natural language inputs. Other updates include a revamped main chat experience, support for conversational flow in Custom Metric skill, consolidation of experiment traces under "Experiment Projects," and per-class calibration metrics and chart. Additionally, SDK Version 7.29.0 allows users to log experiments from previously created dataframes. New content includes video tutorials, paper readings, ebooks, self-guided learning modules, and technical posts on various AI topics.
The AI Agents Masterclass by Jerry Liu and Jason Lopatecki delves into workflows and architectures of AI agents. Event-based systems and graph-based systems are two primary approaches to building AI agents, each with its own advantages and challenges. State management is a crucial aspect in both architectural styles. Production engineering for AI agents involves observability and debugging strategies, as well as performance optimization focusing on areas like routing accuracy, token usage efficiency, response latency, and error handling. The landscape of AI agents is maturing with teams prioritizing focused, reliable components over general autonomy, emphasizing the need to start simple and expand based on real-world usage patterns.
Building an AI agent involves complexities such as testing, iterating, and improving its performance. Tools like Arize and Phoenix are essential for navigating these challenges. During the development phase, Phoenix traces provide valuable insights into how users interact with the AI agent, enabling quick identification of issues and iteration. Once in production, Arize becomes crucial for monitoring user interactions and ensuring the AI agent performs as expected. Daily usage of dashboards helps track high-level metrics such as request counts, error rates, and token costs. Experiments are useful for testing changes like model updates or A/B tests, while datasets help identify patterns and form hypotheses. Automating evaluation workflows using CI/CD pipelines ensures thorough testing with minimal manual effort. Continuous monitoring and troubleshooting involve identifying issues through evals and resolving them in the Prompt Playground before pushing changes to production.
The "Agent-as-a-Judge" framework presents an innovative approach to evaluating AI systems, addressing limitations of traditional methods that focus solely on final outcomes or require extensive manual work. This new paradigm uses agent systems to evaluate other agents, offering intermediate feedback throughout the task-solving process and enabling scalable self-improvement. The authors found that Agent-as-a-Judge outperforms LLM-as-a-Judge and is as reliable as their human evaluation baseline.
Instrumentation is crucial for developers building applications with Language Learning Models (LLMs) as it provides insights into application performance, behavior, and impact. It helps in monitoring key metrics like response times, latency, token usage, detecting anomalies in model responses, tracking resource usage, understanding user behavior, ensuring compliance and auditability, and facilitating continuous improvement of the models. Arize Phoenix is an observability tool that can be integrated with Vercel AI SDK for easy implementation of instrumentation in Next.js applications. The integration involves installing necessary dependencies, enabling instrumentation in Next.js configuration file, creating an instrumentation file, enabling telemetry for AI SDK calls, and deploying the application to monitor its performance using Phoenix UI.
AutoGen is a framework designed for creating multi-agent applications, which involve multiple LLM (Large Language Model) agents working together towards a common goal. These applications often aim to replicate the structure of human teams and organizations. Agents in AutoGen are defined with a name, description, system prompt, and configuration that specifies the LLM to use and any necessary API keys. Tools can be attached to agents as functions they can call, such as connections to external systems or regular code logic blocks. Various interaction structures like two-agent chat, sequential chat, and group chat are supported by AutoGen. The benefits of using AutoGen include easier creation of multi-agent applications, prebuilt organization options, and the ability to handle communication between agents.
OpenAI's Realtime API is a powerful tool that enables seamless integration of language models into applications for instant, context-aware responses. The API leverages WebSockets for low-latency streaming and supports multimodal capabilities, including text and audio input/output. It also features advanced function calling to integrate external tools and services. The Realtime API Console is a valuable resource for developers, offering insights into the API's functions and voice modes. Key API events include session creation, updates, conversation item logging, audio uploads, transcript generation, and response cancellation. Evaluation methods for real-time audio applications involve text-based accuracy checks, audio-specific factors like transcription accuracy, tone, coherence, and integrated audio-text evaluation. Potential use cases of the API include conversational tools, hands-free accessibility features, emotional nuance analysis, voice-driven engagement, and integration with OpenAI's chat completions API for adding voice capabilities to text-based applications.
Safety and reliability are crucial aspects of Language Models (LLMs) as they become increasingly integrated into customer-facing applications. Real-world incidents highlight the need for robust safety measures in LLMs to protect users, uphold brand trust, and prevent reputational damage. Evaluation needs to be tailored to specific tasks rather than relying solely on benchmarks. To improve safety and reliability, developers should create evaluators, use experiments to track performance over time, set up guardrails to protect against bad behavior in production, and curate data for continuous improvement. Tools like Phoenix can help navigate the development lifecycle and ensure better AI applications.
Arize has recently evaluated several large language models (LLMs) for time series anomaly detection, focusing on the o1-preview model. The evaluation involved analyzing hundreds of time series data points from various global cities and detecting significant deviations in these metrics. o1-preview significantly outperformed other models in anomaly detection, marking a leap forward for time series analysis in LLMs. However, its processing speed remains a challenge. Arize Co-pilot's future may include model selection based on task complexity and accuracy requirements, with the potential for swapping models in and out as needed.
Arize has released new features and enhancements, including Copilot skills for custom metric writing and embedding summarization. Local Explainability Report is now available with table view and waterfall style plot for detailed per-feature SHAP values on individual predictions. Experiment Over Time Widget allows users to integrate experiment data directly into their dashboards. Full Function Calling Replay in Prompt Playground enables iterations of different functions within the Prompt Playground. Instrumentation Enhancements include Context Attribute Propagation, Typescript Trace Configuration, Vercel AI SDK integration, and LangChain Auto Instrumentation support for version 0.3. New content includes video tutorials, paper readings, ebooks, self-guided learning modules, and technical posts on Prompt Optimization Course, Evaluation Workflows to Accelerate Generative App Development and AI ROI, Swarm: OpenAI’s Experimental Approach to Multi-Agent Systems, LLM Evaluation Course, and Techniques for Self-Improving LLM Evals.
Arize, an AI observability and evaluation platform, has partnered with Vertex AI API serving Gemini 1.5 Pro to accelerate generative app development and improve AI ROI for enterprises. The integration of these tools allows teams to leverage advanced natural language processing capabilities, enhance customer experiences, boost data analysis, and improve decision-making. Arize's solutions help tackle common challenges faced by AI engineering teams, such as performance regressions, discovering test data, and handling bad LLM responses. By combining Arize with Google's advanced LLM capabilities, enterprises can optimize their generative applications and drive innovation in the rapidly evolving landscape of artificial intelligence.
Prompt caching is a technique used by AI apps to improve speed and user experience. It involves pre-loading relevant information as soon as users start interacting with the app, reducing response times. OpenAI and Anthropic are two major providers offering unique prompt caching solutions. OpenAI's approach automatically stores prompts, tools, and images for a smoother experience, while Anthropic's caching provides more granular control, allowing developers to specify what to cache. Both systems have their strengths: OpenAI is optimal for shorter prompts with frequent requests, offering a 50% cost reduction on cache hits; Anthropic excels with longer prompts and provides more control over cached elements, ideal for apps requiring selective storage. Properly structuring prompts for caching can significantly enhance speed, making AI apps feel magical to users.
OpenAI's Swarm is a lightweight Python library designed to simplify the process of building and managing multi-agent systems. It focuses on educational purposes by stripping away complex abstractions, revealing fundamental concepts of multi-agent architectures. Swarm allows users to define functions and convert them into JSON schema for ease of use. The system routes user requests through agents with specific skill sets represented by tool functions, maintaining context throughout the process. Swarm's approach to control flow sets it apart from other frameworks like Crew AI and AutoGen, which provide high-level abstractions for control flow. Instrumentation and evaluation can be done using Phoenix, providing insights into message history, tool usage, and agent transitions. Overall, Swarm offers an accessible starting point for building effective multi-agent applications by focusing on essential concepts without complex abstractions.
Arize's OpenInference instrumentation has reached one million monthly downloads, marking a significant milestone in observability for AI using OpenTelemetry (OTEL). The journey has been challenging but rewarding as the team, along with other key players in the industry, paves the way for OTEL LLM instrumentation. They faced several challenges such as dealing with latent data, navigating lists in OTEL, avoiding attribute loss, handling futures and promises, managing streaming responses, and defining the scope of instrumentation. Despite these hurdles, Arize remains committed to shaping the future of standardization in LLM observability.
Arize has released new features, including the ability to run tasks once on historical data and filter experiments based on dataset attributes or experiment results. Users can now test a task by running it once on existing data or apply evaluation labels to older traces. Additionally, users can view logs and check if a task is set to run continuously or just once. Experiment filters allow for more precise tracking of experiment progress and identification of areas for improvement. The latest content includes video tutorials, paper readings, ebooks, self-guided learning modules, and technical posts on topics such as tracing LLM function calls, intro to LangGraph, exploring Google's NotebookLM, OpenTelemetry and LLM observability, object detection modeling, and building better AI.
Self-improving LLM evals involve creating robust evaluation pipelines for AI applications. The process includes curating a dataset of relevant examples, determining evaluation criteria using LLMs, refining prompts with human annotations, and fine-tuning the evaluation model. By following these steps, LLM evaluations can become more accurate and provide deeper insights into the strengths and weaknesses of the models being assessed.
LangGraph is a versatile library for building stateful multi-actor applications within large language models (LLMs). It supports cycles, which are crucial for creating agents, and provides greater control over the flow and state of an application. Key abstractions include nodes, edges, and conditional edges, which structure agent workflows. State is central to LangGraph's operation, allowing it to maintain context and memory. Arize offers an auto-instrumentor for Langchain that works with LangGraph, capturing and tracing calls made to the framework. This level of traceability is crucial for monitoring agent performance and identifying bottlenecks. By evaluating agents using LLMs as judges, developers can measure their effectiveness and improve performance over time.
OpenAI's Swarm is a new addition to the multi-agent framework space, offering a unique approach compared to established players like CrewAI and Autogen. While all three frameworks structure agents similarly, they differ in task execution, collaboration methods, memory management, and tooling flexibility. Swarm stands out for its simplicity, using LLM function calls as the primary method of agent interaction. As OpenAI continues to develop Swarm, it may bring new perspectives to multi-agent AI systems.
Google's NotebookLM is a product that has found its niche in transforming text from various formats into engaging podcast-style dialogues. The secret behind its realistic audio generation lies in the SoundStorm model, which uses Residual Vector Quantization (RVQ) and parallel decoding to maintain speaker consistency over extended durations. NotebookLM's attention to human-like details contributes to the authenticity of AI-generated audio content. Potential future applications include personalized advertising and AI-assisted podcasting, but these advancements also raise ethical concerns around content authenticity and intellectual property protection.
OpenTelemetry (OTel) is becoming an essential component in monitoring and optimizing the performance of Language Learning Models (LLMs). It provides observability, allowing developers to see what's happening in their LLM applications, understand issues quickly, evaluate outputs, run root cause analysis, and ultimately improve their systems. OTel is comprised of three parts: traces, metrics, and logs. Traces allow tracking and analyzing the path of a request as it moves through different parts of a distributed system. Using an open standard like OTel ensures that consumers can switch easily in the ecosystem without being tightly coupled to something that isn't standardized or vendor-specific. OpenInference is built on the foundations of OTel, with added semantic conventions specific to LLM observability.
Vectara-agentic is an Agentic Retrieval-Augmented Generation (RAG) package that enables developers to create AI assistants and agents using Vectara. The integration of Arize Phoenix, a leading open-source observability tool, into vectara-agentic allows users to gain insights into the operation of their AI assistants. This combination provides an easy way to develop Agentic RAG applications with added observability capabilities for better understanding and control over the agent's behavior.
Arize has released new features and enhancements for its genAI applications. Embeddings Tracing allows users to select embedding spans and access the UMAP visualizer, simplifying troubleshooting. The Experiments Details page now displays a detailed breakdown of labels for experiments. Prompt Playground Improvements include full support for all OpenAI models, function/tool call support, full-screen data mode, prompt overriding, and pop up windows for long outputs & variables. Additionally, Filter History has been added to store the last three filters used by a user, and Parent Spans have been enhanced on the Traces page. Quick Filters allow users to apply filters directly from the table.
Arize AI and MongoDB have partnered to help AI engineers develop and deploy large language model (LLM) applications with confidence. The combination of MongoDB's vector search capabilities for efficient memory management and Arize AI's advanced evaluation and observability tools enables the building, troubleshooting, and optimization of robust agentic systems. This partnership offers a powerful toolkit for constructing and maintaining generative-powered systems, ensuring effective debugging and optimization in complex architectures like retrieval augmented generation (RAG). Arize AI's platform provides comprehensive observability tools, while MongoDB's document-based architecture supports contextual memory management. The collaboration also offers a library of pre-tested LLM evaluations, interactive RAG strategy capabilities, and compatibility with popular LLM frameworks like LangChain and LlamaIndex. Overall, the Arize AI and MongoDB partnership provides developers with a comprehensive toolkit for building, evaluating, and optimizing their AI agents.
This article discusses best practices for selecting the right model for Language Learning Model (LLM) as a judge evaluations. It emphasizes the importance of using an LLM to evaluate other models, which can save time and effort when scaling applications. The process involves starting with a golden dataset, choosing the evaluation model, analyzing results, adding explanations for transparency, and monitoring performance in production. GPT-4 emerged as the top performer in recent evaluations, achieving an accuracy of 81%. However, other models like GPT-3.5 Turbo or Claude 3.5 Sonnet may also be suitable depending on specific needs. The article suggests using Arize's Phoenix library for pre-built prompt templates and resources to run LLM-as-a-judge evaluations.
OpenAI's latest models, GPT-4o and o1-preview, showcase improved performance on logical reasoning tasks compared to previous models like GPT-3.5. These models are designed for instruction following and can generate more coherent and contextually relevant responses. However, they still face challenges with latency and cost, which may limit their widespread adoption in real-world applications. GPT-4o is a fine-tuned version of GPT-3.5 that demonstrates improved performance on coding tasks, while o1-preview is an experimental model that further enhances logical reasoning capabilities. Arize's benchmarking results show that o1-preview outperforms other models in detecting anomalies within time series data sets. As these models continue to evolve and improve, it will be interesting to see how they are integrated into various applications and industries. OpenAI is likely to focus on optimizing latency and cost for future releases of o1-preview, making it more accessible for real-world use cases.
Reflection tuning is an optimization technique where models learn to improve their decision-making processes by reflecting on past actions or predictions. This method enables models to iteratively refine their performance by analyzing mistakes and successes, thus improving both accuracy and adaptability over time. By incorporating a feedback loop, reflection tuning can address model weaknesses more dynamically, helping AI systems become more robust in real-world applications where uncertainty or changing environments are prevalent. The recent Reflection 70B drama highlights the importance of double checking research results and the potential impact of data quality on LLM performance.
Arize has released AI Search V2, which includes new features such as Column Search (improved), Table Search (new), Text to Filter (new), and LLM Analysis Lite (new). Additionally, Copilot can now answer questions about the Arize product. Experiments Overview Visualization has been enhanced on the Experiment Overview page, allowing users to view up to 10 most recent experiments and select which evaluations they'd like to visualize. Data API now supports querying for drift over time using GraphQL, while Admin API allows querying for organization users, updating space membership, or deleting a user from a space. New content includes articles on tracing Groq applications, composable interventions for language models, and creating and validating synthetic datasets for LLM evaluation & experimentation.
This post provides a step-by-step guide on how to trace a Groq application and visualize telemetry data using Arize Phoenix. The process involves setting up environment variables, launching Phoenix locally, connecting the application to Phoenix, auto-instrumenting with the Groq Instrumentation Package, making model calls, and finally visualizing the traces in Phoenix. The guide highlights the benefits of using Arize Phoenix for tracing, debugging, and evaluating LLM applications, offering insights into system behavior over time.
- The paper presents a study of the composability of various interventions applied to large language models (LLMs). - Composability is important for practical deployment, as it allows multiple modifications to be made without requiring retraining from scratch. - The authors find that aggressive compression struggles with composing well with other interventions, while editing and unlearning can be quite composable depending on the technique used. - They recommend expanding the scope of interventions studied and investigating scaling laws for composability as future work.
Synthetic datasets are artificially created data sources that mimic real-world information for use in large language model (LLM) evaluation and experimentation. They offer several advantages, including controlled environments for testing, coverage of edge cases, and protection of user privacy by avoiding the use of actual data. These datasets can be used to test and validate model performance, generate initial traces of application behavior, and serve as "golden data" for consistent experimental results. Creating synthetic datasets involves defining objectives, choosing data sources, generating data using automated or rule-based methods, and ensuring diversity and representativeness in the data. Validation is crucial to ensure accurate representation of patterns and distributions found in actual use cases. Combining synthetic datasets with human evaluation can improve their overall quality and effectiveness. Best practices for synthetic dataset use include implementing a regular refresh cycle, maintaining transparency in data generation processes, regularly evaluating dataset performance against real-world data and newer models, and taking a balanced approach when augmenting synthetic datasets with human-curated examples. By following these guidelines and staying up to date with emerging research and best practices, developers can maximize the long-term value and reliability of their synthetic datasets for LLM evaluation and experimentation.
Arize has released new features and updates on September 5, 2024. Key highlights include the introduction of Annotations (Beta) for custom labeling data, enhancements to Models API allowing users to query Model Versions and set model baseline using GraphQL, Metrics API update enabling direct queries for average metrics or metrics over time from the model node, new content on Agent Architectures, Evaluating an Image Classifier, Advanced Guardrails, a survey on The State of AI Engineering, LLM Tracing Primer, Bazaarvoice's challenges in deploying an LLM app, and a report on the rise of Gen AI in SEC filings.
This tutorial guides users through setting up an image classification experiment using Phoenix, a multi-modal evaluation and tracing platform. The process involves uploading a dataset, creating an experiment to classify the images, and evaluating the model's accuracy. OpenAI's GPT-4o-mini model is used for the classification task. Users are required to have an OpenAI API key ready and install necessary dependencies before connecting to Phoenix. The dataset is loaded from Hugging Face, converted to base64 encoded strings, and then uploaded to Phoenix. After defining the experiment task using OpenAI's GPT-4o-mini model, evaluators are set up to compare the model's output with the expected labels. Finally, the experiment is run, and users can modify their code and re-run the experiment for further evaluation.
The State of AI Engineering survey reveals that industries are rapidly adopting large language models (LLMs) for various applications such as summarizing medical research, navigating complex case law, and enhancing customer experiences. Over half of the surveyed AI teams plan to deploy small language models in production within the next 12 months. The most common use cases include chatbots, code generation, summarization, and structured data extraction. Privacy concerns, accuracy of responses, and hallucinations are identified as the top implementation barriers for LLMs. Prompt engineering is widely used by AI teams, with nearly one in five relying on LLM observability tools to evaluate and trace generative AI applications. Developers and AI teams show a preference for both open-source and proprietary models, with a slight increase in interest in third-party cloud-hosted options. The majority of respondents are neutral or against more regulation of AI, while Python remains the preferred language for serving LLMs.
Observability is crucial in autonomous AI agents as it allows monitoring and evaluation of their performance. CrewAI is an open-source agent framework that enables the creation and design of personalized employees to automate tasks or run businesses. Key concepts include agents, tasks, tools, processes, crews, and pipelines. Observability can be set up using Arize Phoenix for real-time insights into AI agents' activities and performance. CrewAI offers enhanced visibility, streamlined task management, collaborative intelligence, scalability, and customization options.
In the Arize Release Notes on Aug 23, 2024, users can now create spaces programmatically using graphQL. Online evals have been updated with support for three new LLM integrations: Azure OpenAI, Bedrock, and Vertex / Gemini. Event-based Snowflake jobs are also introduced, allowing users to trigger Snowflake queries via graphQL. The Python SDK v7.20.1 includes enhancements such as delayed tags for stream logging, experiment eval metadata, and ingesting data to Arize using space_id. Additionally, new content has been published on topics like tracing LLM applications, LlamaIndex workflows, types of LLM guardrails, annotations for human feedback, evaluating alignment and vulnerabilities in LLMs-as-judges, Flipkart's use of generative AI, and Atropos Health leveraging LLM observability.
Bazaarvoice, a leading platform for user-generated content and social commerce, has successfully deployed an LLM app after navigating through challenges related to data quality and education. The first challenge was ensuring the data used in retrieval augmented generation (RAG) was clean and accurate, especially when it came to business-specific data. The second challenge involved educating employees about AI's capabilities and limitations. Bazaarvoice has found that AI is transforming its business by improving content quality for clients and enhancing the user experience through generative AI applications like a content coach.
Combining Phoenix with Haystack enables effortless tracing, enhanced debugging, and comprehensive evaluations for LLM applications and search systems. With just a single line of code, Phoenix provides deep insights into application behavior through tracing, allowing developers to pinpoint issues quickly. To set up a basic RAG application using Haystack and Phoenix, install the necessary libraries, launch a local Phoenix instance, connect it to your application, and add the Haystack auto-instrumentor to generate telemetry. Initialize your Haystack environment by setting up a document store, retriever, and reader, and build a RAG pipeline with components such as a retriever, prompt builder, and LLM. Finally, call the Haystack pipeline with a question and view the resulting LLM traces in Phoenix for further analysis.
This paper evaluates the performance of various LLMs acting as judges on a TriviaQA benchmark. The researchers assess the alignment between the judge models' outputs and human annotations, finding that only the best-performing models (GPT-4, Turbo, and Llama 3 7 B) achieve high alignment with humans. The study highlights the importance of using top-performing models for evaluating LLMs as judges. The results also show that larger models tend to perform better than smaller ones, but the difference in performance is not always significant. Additionally, the paper finds that prompt optimization and handling under-specified answers can improve the performance of LLM judges. However, it's essential to note that this study is conducted in a controlled environment and might not generalize well to real-world use cases. The authors recommend using Cohen's Kappa as a metric for evaluating alignment between human evaluators and LLM judges, which accounts for agreement by chance.
Phoenix is an AI development platform that allows users to collect human feedback on their Large Language Model (LLM) applications, making it easier to evaluate and improve these models. The platform provides a robust system for capturing and cataloging human annotations, which can be added via the UI or through SDKs or API. Annotations can be used to create datasets, log user feedback in real-time, and filter spans and traces in the UI or programmatically. Phoenix also integrates with its Datasets feature, allowing users to fine-tune their models using annotated data. The platform enables a new system of collecting human feedback en masse through reinforcement learning from human feedback (RLHF), popularized by the rise of LLM-based evaluations. With Phoenix, users can now log human feedback into their applications, combining automated metrics with human insights to create models that not only perform well but also resonate with users.
Atropos Health is working to close the evidence gap by making observational studies easily accessible for physicians. The company's Principal Data Scientist, Rebecca Hyde, has over ten years of experience in data science and public health. They have developed AutoSummary, an LLM-based prompting tool that produces summaries of these studies, which physicians can edit. To measure the performance of AutoSummary at scale, they are setting up a monitoring framework using LLM Observability. Hyde spoke about her experiences with Arize:Observe, where she learned more about LLMs and their use cases.
The text discusses example notebooks available from OpenInference on various topics such as RAG pipelines and building fallbacks with conditional routing using Haystack, Groq, and other libraries. It also introduces a tutorial on instrumentation for LLM applications, covering key frameworks like OpenTelemetry (Otel) and OpenInference, along with the pros and cons of automatic and manual instrumentation. The tutorial demonstrates three methods for setting up manual instrumentation: using decorators, the `with` clause, and starting spans directly. Additionally, it mentions new content in video tutorials, paper readings, ebooks, self-guided learning modules, and technical posts.
Flipkart has leveraged generative AI to support its 600 million users, with a primary goal of improving customer experience and scaling their business. The company's Head of Applied AI, Anu Trivedi, discussed the challenges of measuring success in product development and how Flipkart is using generative AI to create conversational commerce opportunities. With the help of Arize, a partner that provides traceability, Flipkart has gained the ability to stitch together various metrics and create a storyline to improve their product. The company's experience with generative AI has been a learning process, highlighting the importance of understanding customer bases and finding the right route for solution implementation. By leveraging generative AI, Flipkart aims to make a significant impact on its business, particularly in terms of ROI and GMV.
LlamaIndex has released a new approach to easily create agents called Workflows, which use an event-based architecture instead of traditional pipelines or chains. This new approach brings new considerations for developers and questions on how to evaluate and improve these systems. Workflows are an orchestration approach that lets you define agents as a series of Steps, each representing a component of your application. This event-driven architecture gives more freedom to applications to jump around and allows steps to be self-contained, making it easier to handle intricate flows and dynamic applications. Workflows are great at handling complicated agents, especially those that loop back to previous steps, but may add unnecessary complexity to linear applications. To visualize the paths taken by your Workflows, you can use Arize Phoenix, which provides an integration with LlamaIndex that allows you to easily visualize step-by-step invocations without adding extensive logging code. Workflows can also be evaluated using a similar approach as evaluating any agent, breaking down the process into tracing visibility, creating test cases, breaking components into discrete parts, defining evaluation criteria, running test cases, and iterating on your app.
Llama 3 is a large language model developed by Meta AI that has been trained on diverse data sources with an emphasis on multilingual content. The flagship model of the Llama series, Llama 3-70B, boasts impressive performance in various benchmarks and tasks, including coding, reasoning, and proficiency exams. It also supports eight languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. One of the key features of Llama 3 is its long context window capability, which allows it to retrieve information from large documents effectively. However, it has been found to be more susceptible to prompt injection compared to other models like GPT-4 and Gemini pro. Llama 3's open-source nature makes it accessible for developers and researchers to fine-tune the model according to their needs. Meta AI also released a guardrail model, which can be used as a small model to detect and prevent potential prompt injections or undesired token generations. Overall, Llama 3 showcases significant advancements in large language models and contributes to the growing field of open-source AI development.
Arize AI introduces EU data residency support for all users, allowing them to host their data within the European Union while adhering to local data protection laws such as GDPR. This feature is particularly beneficial for sectors like finance and healthcare where data storage regulations are stringent. Arize is also SOC 2 Type II, HIPAA compliant, and has achieved PCI DSS 4.0 certification. For more information on Arize AI's commitment to compliance and data privacy requirements globally, visit the Arize Trust Center.
This research explores the effectiveness of using Large Language Models (LLMs) as a judge to evaluate SQL generation, a key application of LLMs that has garnered significant interest. The study finds promising results with F1 scores between 0.70 and 0.76 using OpenAI's GPT-4 Turbo, but also identifies challenges, including false positives due to incorrect schema interpretation or assumptions about data. Including relevant schema information in the evaluation prompt can significantly reduce false positives, while finding the right amount and type of schema information is crucial for optimizing performance. The approach shows promise as a quick and effective tool for assessing AI-generated SQL queries, providing a more nuanced evaluation than simple data matching.
With Arize Phoenix, developers can quickly iterate during development and experiment phases of building an LLM application by running it locally, but for production or collaboration, they need to deploy Phoenix. The tool offers data persistence options via SQLite or PostgreSQL databases, enabling the ability to persist application telemetry data and collaborate with colleagues. Phoenix is an open source tool that helps engineers trace, evaluate, and iterate on generative AI applications, providing features such as logging traces, persisting datasets, running experiments, and sharing insights with colleagues.
Developing an AI assistant tailored for data scientists and AI engineers called Arize Copilot involved numerous challenges and valuable lessons about developing with LLMs. The tool is designed to assist users in troubleshooting and improving their models and applications through an agentic workflow, leveraging the Completions API from OpenAI for better control over state management. Lessons learned include managing state effectively, handling model swaps cautiously, using prompt templates with clear instructions and guidelines, incorporating data into prompts in a structured format, configuring function calls explicitly, implementing streaming efficiently, focusing on user experience, and utilizing testing strategies with datasets and automated workflows.
LLM instrumentation is crucial for achieving performance and reliability in large language models. LLM tracing helps track down issues such as application latency, token usage, and runtime exceptions, providing detailed insights into the model's behavior. OpenTelemetry (OTel) enhances tracing by offering standardized data collection and integration with various LLM frameworks. However, OTel may not be suitable for all LLM applications, and manual instrumentation or additional frameworks like OpenInference are necessary to properly instrument an LLM app. Automatic instrumentation offers comprehensive coverage but requires less control over the details of what is traced, while manual instrumentation provides flexible control but demands more effort to implement. Various methods for manual instrumentation, such as using decorators, the `with` clause, and starting spans directly, can be employed to customize tracing in LLM applications.
DSPy Assertions is a programming construct that expresses computational constraints for language model (LM) pipelines, integrated into the recent DSPy programming model. The researchers propose strategies to use assertions at inference time for automatic self-refinement with LMs. They found that LM Assertions improve not only compliance with imposed rules but also downstream task performance, passing constraints up to 164% more often and generating up to 37% more higher-quality responses.
The use of function calling in large language models (LLMs) enables developers to connect LLMs with external tools and APIs, enhancing their utility at specific tasks. However, evaluating the performance of function calls in LLM pipelines is becoming increasingly critical as more applications are deployed into production. Evaluating function calls involves examining each step of the process, including routing, parameter extraction, and function generation. An open source library called Phoenix offers a built-in evaluator to measure the performance of function calling within major LLMs, providing a tool for tracing and evaluation.
Arize AI Copilot is an innovative AI Assistant for AI that provides an intelligent, integrated solution for model and application improvement. It reduces manual effort, accelerates troubleshooting, and offers advanced tools for LLM development and data curation. Key features include versatile skill set, advanced LLM development, prompt optimization, and powerful data curation. Arize Copilot revolutionizes the workflow by integrating traditional processes and automating complex tasks, making it an invaluable assistant for data scientists and AI engineers.
The newly released instrumentation module in LlamaIndex's latest version (v0.10.20) offers a structured and flexible approach to event and span management, replacing the legacy callbacks module. The new module introduces several core components, including Event, EventHandler, Span, SpanHandler, and Dispatcher, designed to optimize monitoring and management of LLM applications. These components work together to provide granular views of application operation, pinpoint notable events, and track sequences of operations within complex applications. By integrating with Arize Phoenix, developers can fine-tune performance, diagnose issues, and enhance the overall functionality of their LLM applications, achieving deeper insights and more effective management of their systems.
The RAFT (Retrieval Augmentation Fine-Tuning) paper presents a method that improves retrieval augmented language models by fine-tuning them on domain-specific data. This approach allows the model to better utilize context from retrieved documents, leading to more accurate and relevant responses. RAFT is particularly useful in specialized domains where traditional document sources may not be effective. The authors demonstrate the effectiveness of RAFT through experiments on various question answering datasets, showing that it outperforms other methods, including GPT-3.5, in most cases.
This article discusses how to manage and monitor open source Language Learning Models (LLMs) applications using UbiOps and Arize. It highlights the benefits of open-source LLMs, such as Llama 3, Mistral, or Falcon, which can be customized easily compared to closed-source models like GPT-4. The article provides a step-by-step guide on deploying an open source LLM (llama-3-8b-instruct) to the cloud with UbiOps and logging prompt and response embeddings together with some metadata to Arize for monitoring purposes. It also explains how to set up a connection with Arize API client, calculate the embeddings using HuggingFace's embedding model, and log the embeddings to Arize. The article concludes by demonstrating how to inspect the results in Arize's platform.
In this paper, the authors propose a method to identify and interpret features in large language models (LLMs) using sparse autoencoders (SAEs). They demonstrate that these features can be used for various applications such as model editing, feature ablation, searching for specific features, and ensuring safety. The main takeaway from this paper is the potential of SAEs to provide a better understanding of LLMs' inner workings, which could lead to more robust and safer models in the future.
Large Language Model (LLM) summarization is a technique that uses advanced natural language processing methods to generate concise and informative summaries of longer texts. It leverages LLMs to comprehend the content of source documents and produce abridged versions that capture key points and main ideas for an LLM system. The benefits of summarization include streamlining information processing, enhancing efficiency in information retrieval, promoting better retention and understanding of materials, leading to improved learning outcomes. There are three primary approaches to LLM summarization: extractive, abstractive, and hybrid. Extractive approach involves selecting and assembling specific sentences or passages from the source document to create a summary. The abstractive approach aims to understand the underlying meaning and concepts expressed in the text, emulating human comprehension. Hybrid approach combines elements of both extractive and abstractive techniques, leveraging their advantages while mitigating their limitations. Challenges in LLM summarization include recursion issues, refine issues, better chunking for summarization, and evaluation. Evaluation generally consists of an evaluation of LLM outputs by using a separate evaluation LLM. The fundamentals of LLM evaluation for production include benchmarking with a golden dataset, leveraging task-based evals, and running across environments. A code walkthrough demonstrates how to perform summarization classification tasks using OpenAI models (GPT-3.5, GPT-4, and GPT-4 Turbo) against a subset of the data from a benchmark dataset. The results show that there is a significant increase in the quality of predictions with each model, with GPT-4 Turbo providing the best performance.
In this paper review, we discussed how to create a golden dataset for evaluating LLMs using evals from alignment tasks. The process involves running eval tasks, gathering examples, and fine-tuning or prompt engineering based on the results. We also touched upon the use of RAG systems in AI observability and the importance of evals in improving model performance.
GetYourGuide powers millions of daily ranking predictions by leveraging Production AI, a machine learning system that meets such demands and maintains performance. The company faced several challenges while building its search ranking system, including diverse feature types, running real-time feature pipelines, cost-efficient serving, A/B testing, drift detection, and data quality monitoring. To tackle these challenges, GetYourGuide adopted Tecton as its feature platform and Arize for model observability, which fit nicely with the organization's existing tech and helped create new features for user personalization while offering a clearer view of how models perform in production and whether any changes in features or model behavior need addressing. The team uses Airflow to orchestrate dataset generation, automate model training, and deploy a fresh model on a daily basis. Tecton's offline store enables GetYourGuide to easily fetch point-in-time accurate feature values for each unique entity at the exact time of historical ranking events. Arize is used to monitor model performance and track Normalized Discounted Cumulative Gain (NDCG) as a primary performance metric, allowing the team to identify areas of improvement and compare different datasets.
Arize AI has partnered with Microsoft Azure to enhance the deployment of large language models (LLMs) in enterprise applications. The collaboration integrates Arize's LLM evaluation and observability platform with Azure's Model as a Service, offering users access to popular open-source models curated by Azure AI. This partnership aims to speed up the reliable deployment of LLM applications while ensuring robust ML and LLM observability for Fortune 500 companies using Azure along with Arize. The integration also provides tools for collecting evaluation data, troubleshooting search and retrieval, and tracing to see where an LLM app chain fails.
Generative AI can be used to evaluate bias in speeches by analyzing the language and content for potentially discriminatory remarks. A custom prompt template was created using OpenAI's GPT-4 model, which identified a section of Harrison Butker's commencement speech as "misogynistic" due to its perpetuation of gender stereotypes. The LLM classified another section of the speech as "homophobic" after identifying derogatory comments and references to Pride Month. These results highlight the potential for generative AI to monitor and mitigate harmful language in various contexts, including online conversations, customer call centers, and public speeches.
The evaluation of large language models (LLMs) is crucial to ensure their reliability and effectiveness in various applications. However, the process of evaluating LLMs can be challenging due to the subjective nature of some criteria and the need for human judgement. In this paper review, we discuss a study that explores the use of LLMs as judges for evaluating other LLMs. The study presents a framework called EvalGen, which aims to improve evaluation metrics by incorporating human feedback and iteratively refining evaluation criteria. The EvalGen framework consists of four main steps: pretest, grading, customization, and implementation. In the pretest step, users define their evaluation criteria and create an initial set of examples with labels. The LLM judge then evaluates these examples based on the defined criteria. In the grading step, human evaluators grade the LLM's performance on the same set of examples to identify any misalignments between the LLM's judgement and human expectations. The customization step involves adjusting evaluation criteria based on feedback from human evaluators. This can include adding or removing criteria, modifying existing criteria, or changing their weightage. The final implementation step incorporates the refined evaluation criteria into the LLM application for continuous monitoring and improvement. One key takeaway from this study is the importance of iterative evaluation and refinement of evaluation criteria to ensure accurate and reliable results. Additionally, the use of golden data sets can help users better understand their evaluation criteria and identify any misalignments between human judgement and LLM performance. While there is some skepticism around using LLMs as judges for evaluating other LLMs, particularly in production environments, this study demonstrates that with proper customization and iteration, LLMs can be effective tools for evaluating LLM applications.
In this paper read, we discussed the use of language models (LLMs) as agents that can interact with external tools and environments to solve complex problems. We covered two main techniques for enabling LLMs to act as agents: ReAct and Reflexion. ReAct is a technique that allows LLMs to generate thoughts, observations, and actions in response to prompts. It involves the use of an actor-evaluator framework, where the actor generates possible actions based on the current state, and the evaluator assesses the quality of these actions before selecting one to execute. Reflexion is a more advanced technique that builds upon ReAct by incorporating self-reflection and memory components. It enables LLMs to evaluate their own actions and learn from past experiences, making them more effective problem solvers over time. We also touched on the concept of chain of thought, which prompts LLMs to verbalize their intermediate reasoning steps when solving multi-step problems. This technique can help improve transparency and reduce hallucination errors in LLM outputs. Overall, these techniques demonstrate how LLMs can be leveraged as powerful agents capable of handling complex tasks by interacting with external tools and environments.
The article provides four tips on how to effectively read AI research papers. Firstly, it suggests following the right people in the industry to stay updated with the latest research. Secondly, it advises identifying the type of paper and breaking it down accordingly. There are three general categories: surveys, benchmarking and dataset papers, and breakthrough papers. Each category has its own purpose and value for readers. Thirdly, being an active reader is recommended by constantly questioning and validating findings. Lastly, following real-time progress in the field can help keep up with rapid changes and developments.
Amazon's Chronos is a time series model framework that leverages language model architecture and training on billions of tokenized time series observations to provide accurate zero-shot forecasts, often matching or exceeding purpose-built models. The framework exploits sequential similarities between language and Time Series models by scaling and quantizing the data, then using a classification approach to learn distributions. Chronos has been shown to be less accurate and slower than traditional statistical models in some cases, but its potential for improving forecasting accuracy with large-scale computational resources is still being explored. The model's performance depends on various factors, including the quality of the training data, the choice of hyperparameters, and the specific use case. While Chronos shows promise, it is not yet a replacement for traditional Time Series models, and further research is needed to improve its accuracy and efficiency.
Anthropic's Claude 3 is a new family of models in the LLM space that challenges GPT-4 with its high performance and capabilities. The three models in this family - Haiku, Sonnet, and Opus - offer different balances of intelligence, speed, and cost. Claude 3 has made significant improvements over its predecessor, Claude 2, particularly in terms of latency and vision capabilities. However, the model still requires careful prompting to achieve optimal results, and its performance can vary depending on the task and prompt used. The model's ability to detect and respond to toxic requests is also an area where it excels. Despite being a new model, Claude 3 has already garnered significant attention in the community, with some users praising its writing style and others expressing frustration with its limitations. As with any new technology, there is still much to be learned about how to effectively use and evaluate Claude 3 for various tasks.
This tutorial demonstrates how to set up a SQL router query engine for effective text-to-SQL using Large Language Models (LLMs) with in-context learning. It builds on top of LlamaIndex, a table of cameras, and a vector index built from a Wikipedia article to make routing decisions between SQL retriever and embeddings. The tutorial covers how to install dependencies, launch Phoenix, enable tracing within LlamaIndex, configure an OpenAI API key, prepare reference data, build the LlamaIndex application, and make queries using the router query engine. It highlights the importance of LLM tracing and observability in finding failure points and acting on them quickly. The implementation can lead to inconsistent results due to the influence of the SQL tool description on the router's choice of tool, emphasizing the need for careful tuning and monitoring.
In this paper review, we discussed the use of reinforcement learning in language models (LLMs) and how it can be used to improve their performance. The main idea is to provide feedback to the model based on its responses to prompts, which helps guide the model's behavior towards a desired outcome. We also talked about the challenges involved in this process, such as credit assignment and prompt optimization. Overall, reinforcement learning has the potential to significantly enhance LLMs by enabling them to learn from experience and adapt their responses accordingly.
The text discusses Retrieval Augmented Generation (RAG), a technique that enhances the output of robust language models by leveraging external knowledge bases. RAG involves five key stages: loading, indexing, storing, querying, and evaluation. The text also covers how to build a RAG pipeline using LlamaIndex and Phoenix, a tool for evaluating large language model performance. The pipeline is evaluated using metrics such as NDCG, precision, and hit rate, which measure the effectiveness of retrieving relevant documents. Additionally, the text discusses response evaluation, including QA correctness, hallucinations, and toxicity. The evaluations provide insights into the RAG system's performance, highlighting areas for improvement.
This text discusses OpenAI's Text-to-Video Generation Model, Sora, and its implications on the industry. Sora is capable of generating high-fidelity videos up to a minute long while maintaining visual quality and adherence to user prompts. The model uses a transformer architecture and space-time patches of video with latent image codes. It has the ability to generate animations and has been praised for its motion quality, but also has limitations in terms of physics-based simulation. The paper also explores the concept of EvalCrafter, a framework for benchmarking and evaluating large video generation models, which includes metrics such as video quality, text-to-video alignment, temporal consistency, and pixel-wise differences between warped images and predicted images. The researchers discuss the challenges of creating quantitative measures for video evaluation, particularly when it comes to human interpretation and feedback. They also highlight the importance of model evaluations in understanding the quality of generated videos and comparing models. Additionally, they mention the potential applications of Sora in industries such as animation, gaming, and advertising, where high-quality video generation is crucial. Overall, the discussion focuses on the technical aspects of Sora and EvalCrafter, highlighting their capabilities and limitations, and exploring the future directions for research and development in this field.
Klick Health, the world's largest independent commercialization partner for healthcare and life sciences, is pioneering AI-powered applications to accelerate growth and improve experiences and outcomes for patients and consumers. As a Data Science Team Leader at Klick Consulting, Peter Leimbigler leads a team that helps define and solve complex problems in the healthcare and life sciences space across various clients. The company has established a generative AI center of excellence to support its own internal operations and client-facing projects, and is exploring ways to use large language models (LLMs) effectively and responsibly. LLMs have shown promise in speeding up drug discovery and development, supporting clinical trials, augmenting doctor-patient interactions, and personalizing patient experiences. However, governing LLM behavior poses unique challenges, particularly in the heavily regulated area of healthcare. Klick has adopted tools like Phoenix for LLM observability to address these challenges and ensure that its AI applications deliver tangible business impact. The company prioritizes outcomes over optics, focusing on reproducibility, transparency, and clarity of data narratives to make meaningful work and collaborate with clients to achieve business results.
Ragas provides a robust workflow for building and evaluating RAG pipelines, utilizing open-source libraries such as Ragas, Arize AI's Phoenix, and LlamaIndex. The pipeline involves generating synthetic test data using Ragas, building a simple RAG application with LlamaIndex, launching Phoenix to collect traces and spans, and evaluating the performance of the LLM application using Ragas. Additionally, Phoenix provides visualization tools for analyzing embedded queries and retrieved documents, allowing developers to identify areas of poor performance and gain insights into their application's behavior. By combining Ragas and Phoenix, developers can create a comprehensive evaluation framework for their RAG pipelines, ensuring high-quality responses and efficient model development.
In the evaluation of retrieval-augmented generation (RAG), the focus is often on the retrieval stage while the generation phase receives less attention. A series of tests were conducted to assess how different models handle the generation phase, and it was found that Anthropic's Claude outperformed OpenAI's GPT-4 in generating responses. This outcome was unexpected as GPT-4 usually has a strong lead in evaluations. The verbosity of Claude's responses seemed to support accuracy, as the model "thought out loud" to reach conclusions. When prompted to explain itself before answering questions, GPT-4's accuracy improved dramatically, resulting in perfect responses. This raises the question of whether verbosity is a feature or a flaw. Verbose responses may enable models to reinforce correct answers by generating context that enhances understanding. The tests covered various generation challenges beyond straightforward fact retrieval and showed that prompt design plays a significant role in improving response accuracy. For applications that synthesize data, model evaluations should consider generation accuracy alongside retrieval.
The paper "RAG vs Fine-Tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture" explores the use of retrieval augmented generation (RAG) and fine-tuning in large language models. It presents a comparison between RAG and fine-tuning for generating question-answer pairs using high-quality data from various sources. The authors discuss the benefits and drawbacks of both approaches, emphasizing that RAG is effective for tasks where data is contextually relevant, while fine-tuning provides precise output but has a higher cost. They also highlight the importance of using high-quality data sets for fine-tuning and suggest that smaller language models may be more efficient in certain cases. The paper concludes by stating that RAG shows promising results for integrating high-quality QA pairs, but further research is needed to determine its effectiveness in specific use cases.
In this paper review, we discussed the recent release of Phi-2, a small language model (SLM) developed by Hugging Face and AI21 Labs. We covered its architecture, training data, benchmarks, and deployment options. The key takeaways from this research are: 1. SLMs have fewer parameters than large language models (LLMs), making them more efficient in terms of memory usage and computational resources. 2. Phi-2 is trained on a diverse range of text data, including synthetic math and coding problems generated using GPT-3.5. 3. The model demonstrates competitive performance on various benchmarks, such as MMLU, HellaSwag, and TriviaQA, while being smaller in size compared to other open-source models like LLaMA. 4. Deployment options for Phi-2 include using tools like Ollama and LLM studio, which allow users to run the model locally on their hardware or even host it as a server. 5. There is ongoing research into extending the context length of SLMs through techniques like self-context extension, which could lead to more advanced applications in the future.
A well-developed data strategy is crucial for businesses to manage their data assets effectively and achieve their objectives. Key considerations include defining a vision for data, assessing current data assets, determining the right technology and tools, and building a data-driven culture. Creating a data moat involves identifying valuable first-party data sources, enriching existing data with external sources, and prioritizing data engineering efforts. To measure the ROI of their data strategy, companies should run data teams as profit centers, build an incremental roadmap, and avoid overspending on infrastructure. A successful data initiative requires a cultural shift within an organization supported by strong leadership and a clear vision.
The top AI conferences in 2024 are expected to be highly attended by industry leaders, researchers, and practitioners from around the world. Conferences like AI Engineer World's Fair, Arize:Observe, Cerebral Valley, World AI Conference, and NVIDIA GTC will focus on various aspects of artificial intelligence, including generative AI, machine learning, data science, and more. These events provide opportunities for networking, skill-building, and staying up-to-date with the latest advancements in AI. Many conferences are expected to take place in major cities like San Francisco, New York, Paris, and Las Vegas, offering a platform for attendees to engage with leading experts, professionals, researchers, and entrepreneurs. The conferences cover various topics, including AI ethics, data analytics, machine learning, deep learning, computer vision, natural language processing, and more. They also provide hands-on workshops, training sessions, and networking opportunities, making them valuable resources for anyone looking to learn about AI or advance their career in the field.