In the field of large language models (LLMs), there has been a shift from dense architectures, where all neurons participate in processing each piece of information, to mixture-of-experts (MoE) architectures, which allow for more efficient use of resources. MoE architecture involves a gating network that decides which "experts" to route tokens to based on their content. This enables the model to focus its computational power on relevant areas while reducing overall compute time and cost. Mistral 8X7B is an example of an LLM utilizing MoE architecture, with a total of 46.7 billion parameters distributed across eight "experts." The non-feedforward blocks are executed for each token, resulting in only two out of the eight experts being utilized per token. This allows for more efficient use of resources and faster inference times compared to dense models like Llama 2 70B. However, there are limitations to this approach, particularly when it comes to knowledge compression within the model. Due to having fewer parameters than some other LLMs, Mistral may not perform as well on tasks that require extensive knowledge storage and retrieval. Further research is needed to optimize MoE architectures for various applications and improve their overall performance.
The adoption of generative AI tools, particularly large language models (LLMs), is rapidly increasing among enterprise engineering teams. Many early adopters are facing challenges such as evaluation, hallucinations, and abstraction issues, but those successfully deploying LLMs are adopting an agnostic approach to connect with major foundation models and tools, operationalizing scientific experiments through independent evaluations, and quantifying ROI and productivity gains by implementing systems for detecting performance issues and proactively addressing them.
In the paper "How to Prompt LLMs for Text-to-SQL," Shuaichen Chang and his co-author investigate the impact of prompt constructions on the performance of large language models (LLMs) in the text-to-SQL task. They focus on zero-shot, single-domain, and cross-domain settings and explore various strategies for prompt construction, evaluating the influence of database schema, content representation, and prompt length on LLMs' effectiveness. The findings emphasize the importance of careful consideration in constructing prompts, highlighting the crucial role of table relationships and content, the effectiveness of in-domain demonstration examples, and the significance of prompt length in cross-domain scenarios.
This blog post benchmarks OpenAI's GPT models with function calling and explanations against various performance metrics, focusing on correctly classifying hallucinated and relevant responses. The results show trade-offs between speed and performance for different LLM application systems. GPT models with function calling tend to have a slightly higher latency than LLMs without function calling but perform on par with them. For model predictive ability on relevance, GPT-4 performs the best overall, while for hallucinations, GPT-4 correctly identifies more often across precision, accuracy, recall and F1 than GPT-4-turbo. The use of explanations does not always improve performance. When deciding which LLM to use for an application, benchmarking and experimentation are required, considering the latency of the system in addition to the performance of relevant prediction metrics.
The recent workshop by Arize AI and PromptLayer on "Prompt Templates, Functions, and Prompt Window Management" provided valuable insights into prompt engineering, a crucial discipline that bridges the gap between raw model capabilities and practical applications. Key takeaways from the event include the importance of iteration in prompt refinement, understanding and mitigating drift, evolving evaluation tools and methodologies, systematic approach to prompt management, and balancing engineering practice with LLMs. The insights shared by the speakers emphasized the need for a structured, adaptive, and systematic approach to navigate the complexities inherent in language models and prompt development effectively.
Language models linearly represent truth or falsehood in factual statements and have a unique structure that can be extracted using mass-mean probing, a novel technique that generalizes better than traditional probing methods. The paper presents evidence of this structure and shows how it can be used to improve the reliability of language models. The authors' goal is to develop a way for humans to access what AI systems know about truth and falsehood, which would enable more accurate evaluations of their outputs. The research has implications for the development of more reliable LLMs and addressing the scalable oversight problem as AI systems become more capable.
This tutorial demonstrates how to ingest large volumes of data, upload it to a vector database like Weaviate, run top K similarity searches against it, and monitor it in production using VectorFlow, Arize Phoenix, LlamaIndex, and other open-source tools. The process involves setting up a vector database, embedding the data with VectorFlow, querying the corpus with LlamaIndex, visualizing the data with Arize Phoenix, and adjusting configurations as needed for optimal results.
The paper "Towards Monosemanticity: Decomposing Language Models With Dictionary Learning" presents a novel approach to understanding interpretability inside large language models (LLMs). It proposes using sparse autoencoders to extract features that represent human-level ideas from the activations of neurons within an LLM. The authors argue that many neurons are polysemantic, meaning they can fire intensely for different tokens such as Arabic text or numbers. They introduce the concept of monosemanticity, which refers to a singular aspect of reality and is what the paper sets out to find. The problem set up involves training an autoencoder on the activations of neurons in a simple transformer network with a single layer NLP multilayer perceptron. The authors use dictionary learning techniques to identify features that represent human discernible concepts or ideas within the model's embeddings. They argue that these features can be thought of as basis vectors that span the vector space of activations, and they can be combined to create more complex features. The paper also discusses the idea of universality in topological structures learned by models, suggesting that different transformers or LLMs trained on various data sets might learn similar topologies. This opens up a new area of research into understanding how ideas are represented within these models and whether there is a common structure to them. Overall, this paper provides valuable insights into the interpretability of large language models and offers an interesting approach to understanding their inner workings.
This paper proposes a new approach to understanding large language models (LLMs) by using dictionary learning and sparse autoencoders. The authors aim to find monosemantic features, or units that represent a single aspect of reality, in the activations of LLMs. They use a simple transformer model as input to their method and train an autoencoder on the neuron activations. The autoencoder is designed to be overcomplete, meaning it has more neurons than necessary to capture the information in the data. This allows the authors to recover a set of dictionary basis features that represent human-like concepts. The paper demonstrates the effectiveness of this approach by extracting features from a variety of datasets, including Arabic text and numbers. The authors also explore the polysemanticity of neurons, where multiple neurons can fire for different reasons, and show how their method can capture these complexities. The work has implications for tasks such as code generation, sentiment analysis, and topic modeling. While the paper does not claim to have solved all interpretability problems in LLMs, it makes a significant contribution to the field by providing a new method for understanding the internal workings of these models.
A recent survey of over 350 AI professionals shows that adoption of large language models (LLMs) is accelerating, with 61.7% of developers and machine learning teams planning to have an LLM app in production within a year or faster. OpenAI remains the dominant player, but alternatives like Meta's Llama 2 are gaining popularity. Concerns about data privacy and responsible deployment are decreasing, while barriers such as "require on-prem" and "accuracy of responses and hallucinations" are increasing. Prompt engineering is the most common implementation method for LLMs, and retrieval augmented generation (RAG) is the most popular use case among teams planning to leverage LLMs. The survey indicates that LLM adoption is not a passing trend and highlights the growing need for tools like LLM observability to ensure companies can maximize the benefits of these models.
The benefits of model observability are significant, with high returns on investment (ROI) due to its ability to preemptively detect and fix model issues impacting business value. Model insights can be automatically detected through Arize monitors and then root-caused through data exploration in interactive guided workflows. The study found that 95% of teams can find a valuable insight when first exploring their data in Arize, with users uncovering an initial insight within the first 24 hours. Proper monitoring coverage allows for automatic detection of model insights, which can be detected once a month and resolved within 24 hours. Observability initiatives provide cost savings by correlating improvements back to business metrics, enabling easy calculations of the value of observability and individual insights for each project and model. Arize offers training and guides on monitoring best practices, and custom metrics allows users to define any metric using model data and metadata to track personalized metrics.
In this paper, the authors propose RankVicuna, an open-source model for document re-ranking that achieves comparable performance to proprietary models like GPT-3.5 and GPT-4 while being significantly smaller in size (7 billion parameters compared to 175 billion). The model is deterministic, ensuring consistent output format and rankings across different runs. RankVicuna uses a teacher-student paradigm for data augmentation, generating query-document pairs from a larger model and shuffling the input order of documents to provide more examples for training. The authors also highlight the importance of prompt engineering in achieving stable results. Overall, this paper showcases the potential of open-source large language models for document re-ranking tasks and emphasizes the role of data augmentation and prompt engineering in improving model performance.
In this paper, the authors propose RankVicuna, an efficient and deterministic reranking model for large language models (LLMs). The model is based on Vicuna, which has been fine-tuned using instruction data from Open Assistant. The main advantage of RankVicuna is its smaller size compared to proprietary models like GPT-3.5 and GPT-4, while still achieving comparable performance in terms of ranking metrics such as NDCG@10 and MAP@100. The authors also highlight the importance of data augmentation for ensuring stability in document reordering. They demonstrate that using a teacher model to generate training data examples from a larger model can improve performance, especially when dealing with smaller datasets. Additionally, they showcase the effectiveness of prompt engineering in achieving stable outputs and reducing hallucinations. Overall, RankVicuna offers an open-source alternative for reranking LLM outputs, which could be particularly useful for teams that do not have access to proprietary models like GPT-3.5 or GPT-4. However, further research is needed to evaluate the model's speed and scalability in production settings.
Microsoft Presidio is an open-source project aimed at ensuring proper management and governance of sensitive data, including PII (personally identifiable information). It uses mechanisms like entity recognition, regular expressions, rule-based logic, checksum with relevant context in multiple languages, and external PII detection models. The two main components are AnalyzerEngine, which scans text to identify PII, and AnonymizerEngine, which replaces identified PII with anonymized values. Presidio can be used to anonymize conversations in a chatbot system by importing necessary dependencies, initializing the analyzer and anonymizer, creating a function that finds and redacts important PII, and running this function on each row of a pandas dataframe to create a new column with anonymized data.
Explaining Grokking Through Circuit Efficiency" is a research paper exploring novel predictions about grokking in neural networks, providing significant evidence in favor of its explanation. The authors demonstrate two surprising behaviors: ungrokking, where a network regresses from perfect to low test accuracy, and semi-grokking, where a network shows delayed generalization to partial rather than perfect test accuracy. The paper discusses the concept of "circuits" within neural networks, which refer to modules that can learn multiple different ways of achieving low loss in parallel. The authors argue that efficiency is independent of training size and that there is a crossover point beyond which the network's performance improves dramatically. They also propose a novel prediction about grokking, which they show is supported by their analysis. The paper highlights the importance of understanding generalization and the challenges associated with it, particularly in the context of large language models like GPT-4. The authors discuss potential applications and open questions related to grokking and efficiency in neural networks.
Arize Phoenix is an open-source library that provides visualizing datasets and troubleshooting large language model (LLM) application development by making it easier to debug and troubleshoot LLM framework applications. It offers flexible data frameworks to connect private data to LLMs, enabling developers to gain visibility into their system with LLM orchestration frameworks like LlamaIndex, Microsoft's Semantic Kernel, and LangChain. Arize Phoenix provides a comprehensive view of the inner workings of an LLM application by breaking down the process into spans and categorizing each span with a common interface across frameworks, making troubleshooting and optimization easier and more effective. The library offers various features such as tracing, evaluating, and analyzing LLM applications to surface problems at different levels of the system, including prompt templates, token usage, runtime exceptions, retrieved documents, embeddings, LLM parameters, tool descriptions, and LLM function calls. It also supports all common spans and has a native integration into LlamaIndex and LangChain, enabling developers to get started with Arize-Phoenix in a few minutes.
At Ray Summit 2023, Anyscale Endpoints debuted with a promise of enabling fast, cost-efficient, and scalable integration of large language models (LLMs) into applications using popular LLM APIs. Arize AI is a launch partner, providing developers with LLM observability across various use cases on any cloud as their AI applications evolve. The service integrates with LangChain, allowing users to fine-tune and deploy powerful open-source LLMs at scale. Users can log LLM responses and metadata into Arize, enabling better evaluation and troubleshooting of LLMs in real-world environments, which is particularly important as the open source LLM ecosystem expands.
The paper discusses the limitations of large language models (LLMs) in effectively predicting and optimizing user behavior, particularly in terms of communication effectiveness. The authors propose a new approach called Large Content Behavior Models (LCBMs), which incorporates behavioral tokens into LLM training corpora to improve performance. They draw parallels between LCBMs and information theory, specifically Claude Shannon's seminal work on communication. The authors demonstrate the effectiveness of LCBMs in better simulating content understanding and behavior understanding compared to traditional LLMs. However, they also acknowledge potential issues with data quality, noise, and ethics in using behavioral tokens for prediction purposes. The discussion highlights the need for more research on the intersection of AI, communication, and information theory, as well as the importance of considering human behavior and ethics in AI development.
Skeleton-of-Thought approach aims to reduce large language model latency while enhancing answer quality by guiding LLMs to construct answer skeletons before parallel content elaboration, achieving impressive speed-ups of up to 2.39x across 11 models. This innovative methodology is similar to writing an outline on a given topic and relies on the chain-of-thought approach that encourages generative AI to showcase its presumed logic when answering a question or solving a problem. The method is data-centric, relying on prompt engineering to accelerate off-the-shelf LLMs without any changes to their model or hardware. SoT has been tested across 11 models and shows significant speed-up potential for common sense knowledge generation, with some question types achieving higher relevance and diversity in answer quality. However, the approach struggles with math questions due to its reliance on context from previous steps, which is not applicable in step-by-step reasoning tasks like math problems. Future work aims to explore trigger mechanisms for specific question types, develop a graph-of-thought architecture that mimics human thought processes, and potentially replace the attention mechanism with alternative architectures. The approach has potential applications in general chatbot systems, improving user experience and lowering system costs by parallelizing content elaboration between segments of a question or multiple questions.
In this paper, the authors propose a method for extending the context window of pre-trained language models without any additional training or modification of the model architecture. The proposed method, called Positional Interpolation (PI), is based on the observation that positional embeddings in transformer models can be interpolated to extend the range of attention scores beyond the original sequence length. The authors first provide a mathematical analysis of why existing methods for extending context windows, such as RoPE, fail to generalize well outside the trained window size. They show that while positional embeddings are designed to capture relative positions within the sequence, they can lead to catastrophic issues when used beyond their intended range. To address this issue, the authors propose PI, which involves interpolating the pre-trained positional embeddings based on their relative positions in the extended window. This approach effectively extends the attention scores to cover the entire sequence, allowing the model to attend to tokens outside its original training context. The authors demonstrate the effectiveness of PI through a series of experiments on various language modeling tasks and benchmarks. They show that using PI with pre-trained Llama models can significantly improve performance on long context windows while maintaining or even improving performance on shorter contexts. Overall, this paper presents an elegant solution for extending the context window of transformer models without requiring any additional training or modification of the model architecture. The proposed method has the potential to enable new applications and improvements in various natural language processing tasks that require long-range dependencies and understanding of context.
The text provides a comprehensive guide on how to thrive during your first tech internship. It emphasizes the importance of networking at career fairs and hackathons, showing initiative in follow-ups and interviews, and maintaining a learning mindset throughout the process. The author also shares their experience working at Arize, an AI startup, highlighting the importance of embracing the company's culture, taking ownership of projects, collaborating with different teams, and enjoying the time spent learning new skills. The text concludes by acknowledging the support from the team at Arize and expressing gratitude for the opportunity to gain first-hand knowledge in AI and machine learning.
Modelbit and Arize's new integration allows for rapid deployment of machine learning (ML) models into production with just one line of code. This enables teams to monitor and fine-tune their ML models instantly, saving time and effort compared to building custom pipelines from scratch. The integration involves setting up a notebook environment, adding Arize keys to Modelbit, defining functions that log inference results to Arize, and deploying the inference function to Modelbit. With this integration, teams can now easily monitor, troubleshoot, and fine-tune their models running in production, as well as detect issues and automate model retraining. Both Modelbit and Arize offer free accounts for users to try out the integration.
This paper introduces Llama 2, a collection of pre-trained and fine-tuned large language models with parameters ranging from 7 billion to 70 billion. The fine-tuned model, Llama 2-Chat, is designed for dialogue use cases and showcases superior performance on various benchmarks. The authors emphasize the importance of safety considerations in large language models, highlighting the need for transparency in training data, human evaluations, and reinforcement learning with human feedback. They also discuss the potential trade-off between helpfulness and safety, suggesting that as a model becomes more helpful, it may become less safe. Llama 2 is released under an open license, allowing users to fine-tune the model on specific domains. The authors aim to promote the use of open-source models and encourage transparency in large language model development.
In this paper reading session, Sally-Ann DeLucia and Amber Roberts discuss the paper "Improving Language Model Retrieval with Query-Aware Contextualization" by OpenAI's team. The paper focuses on improving retrieval performance in large language models (LLMs) by manipulating the context given to them. Key takeaways from this discussion include: 1. Encoder-decoder models have a bidirectional encoder that allows for better understanding of context based on preceding and future tokens, which can be leveraged to improve retrieval performance in LLMs. 2. Placing the query or question before and after the document can significantly improve retrieval performance in LLMs. 3. The architecture of transformers may change as more research is conducted into understanding how these models use context. 4. Pushing relevant information to the top and returning fewer documents are promising strategies for improving retrieval performance in LLMs. 5. Observability tools can be helpful in understanding how these models use context and can aid in experimentation with different architectures.
The collaboration between Snowflake and Arize aims to enhance the machine learning (ML) toolchain by streamlining data access, analysis, and insights. This partnership enables customers to use Arize's advanced AI observability features with Snowflake's simplified data management capabilities. By integrating these two platforms, users can create a fully manageable, simple, and scalable data pipeline that automatically extracts model insights and boosts ROI without compromising security or governance standards. The integration also allows for real-time, continuous monitoring of ML models to ensure optimal performance and proactive troubleshooting.
Orca is a 13-billion parameter model that learns to imitate the reasoning process of large foundation models (LFMs) like GPT-4, surpassing state-of-the-art models by over 100% in complex zero-shot reasoning benchmarks. The paper addresses challenges faced by smaller models such as limited imitation signals, homogeneous training data, and lack of rigorous evaluation. Orca leverages rich signals from GPT-4 to enhance model capabilities and skills through learning from step-by-step explanations generated by humans or advanced AI models.
Mark Scarr is the Senior Director of Data Science at Atlassian, where he leads the Core Machine Learning Team. The team works on various projects across the organization, including marketing and growth/product teams. They have worked on recommendation engines, propensity modeling, and generative AI space. Atlassian's primary machine learning use cases include harvesting keywords for performance marketing and search bidding optimization, customer lifetime value modeling, and building a framework to augment existing keyword pools. Cloud migration has been a game-changer for the business opportunities, providing richer data sets for model training. The team collaborates closely with business stakeholders and analytics teams to ensure models are aligned with business objectives. Mark Scarr is excited about the adoption of LLMs and their potential applications in Atlassian's products, particularly in areas like text summarization, auto-completion, and generating tickets in Jira. He emphasizes the importance of flexibility, adaptability, and embracing new technologies in machine learning. The team is always open to hiring candidates with robust backgrounds in different realms of machine learning.
In this paper reading session, we discussed "GLoRA: Parameter-Efficient Fine-Tuning for Vision and Language Models" by Zhang et al. The main takeaways from the paper are as follows: 1. GLoRA is a parameter-efficient fine-tuning method that builds upon six previous efficient fine-tuning methods, including LoRA, AdapterFusion, VPT, Scaling & Shifting features, and RepAdapter. 2. The main advantage of GLoRA over other fine-tuning methods is its ability to both fine-tune the weight space and the feature space, addressing some limitations of previous methods. 3. GLoRA can be easily expressed as a unified mathematical equation, allowing for an expanded search space without significantly increasing the number of parameters. 4. Experimental results show that GLoRA outperforms other fine-tuning methods in terms of performance and efficiency on both vision and language tasks. 5. The main benefits of using GLoRA are its flexibility, adaptability to a variety of tasks and data sets, and the ability to make more nuanced adjustments during fine-tuning. 6. However, there is still room for improvement in terms of reducing training time and exploring new domains for GLoRA. 7. The paper also highlights that parameter-efficient fine-tuning methods like LoRA and GLoRA are becoming increasingly popular due to their ability to save money and time while achieving better performance than traditional fine-tuning methods.
HyDE is an innovative zero-shot learning technique that combines GPT-3's language understanding with contrastive text encoders, revolutionizing information retrieval and grounding in real-world data. It generates hypothetical documents from queries and retrieves similar real-world documents, outperforming traditional unsupervised retrievers and rivaling fine-tuned retrievers across diverse tasks and languages. HyDE efficiently retrieves relevant real-world information without task-specific fine-tuning, broadening AI model applicability and effectiveness.
HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels is a technique that combines GPT-3's language understanding with contrastive text encoders to revolutionize information retrieval and grounding in real-world data. It generates hypothetical documents from queries and retrieves similar real-world documents, outperforming traditional unsupervised retrievers across diverse tasks and languages. This leap in zero-shot learning efficiently retrieves relevant real-world information without task-specific fine-tuning, broadening AI model applicability and effectiveness. The technique uses a synthetic generation approach to sidestep the problem of relevance labels, generating hypothetical documents that capture structural relevance despite factual inaccuracies. It is particularly useful for applications where relevance labels are scarce or unavailable, such as in search and retrieval tasks. The authors compare HyDE to fine-tuned retrievers, demonstrating its effectiveness in retrieving relevant real-world information without requiring task-specific fine-tuning. They also discuss the importance of structure in text feeding into this approach, noting that it can be a valuable alternative to traditional relevance labels or fine-tuning for generating hypothetical documents.
This blog post discusses how to troubleshoot Large Language Model (LLM) summarization tasks using Arize-Phoenix, an open-source library offering ML observability in a notebook for surfacing problems and fine-tuning generative LLM models. The tutorial guides the reader through analyzing prompt-response pairs, computing ROUGE-L scores, and leveraging Phoenix to find the root cause of performance issues in an LLM summarization model. By following these steps, the reader can identify specific areas where the LLM is struggling and take corrective actions to improve its performance, such as modifying prompt templates or excluding articles from certain languages. The tutorial concludes by highlighting the importance of monitoring LLM performance and identifying specific areas of weakness to improve overall model performance.
Voyager is an LLM-powered embodied agent that autonomously explores the Minecraft world, acquiring skills and making discoveries without human intervention. It outperforms previous approaches by achieving exceptional proficiency in Minecraft and successfully applying its learned skills to solve novel tasks in different Minecraft worlds. The key components of Voyager include an automatic curriculum generation, building a skill library, and iterative prompting mechanisms for feedback and improvement. Observability challenges arise from hallucinations and the need for human intervention in certain cases.
LoRA, or Low-Rank Adaptation of Large Language Models, is a technique that reduces the number of trainable parameters for downstream tasks by freezing pre-trained model weights and injecting trainable rank decomposition matrices into each layer of the Transformer architecture. This approach greatly reduces the number of parameters required for fine-tuning, making it more feasible to deploy large language models in real-world applications. The authors argue that most existing fine-tuning methods are unattractive options, as they either introduce inference latency or result in a fine-tune model that doesn't compare strongly against the full baseline tuning. LoRA achieves better performance than these methods by representing the weight updates in a lower-dimensional space using matrix decomposition, specifically singular value decomposition (SVD). This approach allows for significant reduction in memory usage and training time. The authors demonstrate that LoRA can be used to fine-tune large language models on specific tasks, such as human language to SQL translation, with improved performance compared to existing methods. However, the technique has limitations, including the need to carefully select which adapter matrices to use and potential issues with stacking multiple adapters. Despite these challenges, LoRA has the potential to revolutionize the deployment of large language models in real-world applications by reducing the complexity and cost associated with fine-tuning.
In this discussion, we dive into the concept of Retrieval-Augmented Generation (RAG), a technique that combines parametric and non-parametric memory to improve language generation tasks. We explore the RAG architecture, which consists of two main components: a retriever and a generator. The retriever selects relevant documents from an external knowledge base, while the generator uses these documents along with the input query to generate a response sequence. We discuss how RAG can be used for open-domain question answering tasks, where it outperforms large state-of-the-art language models like GPT-2 and T5. We also examine the differences between RAG sequence and RAG token approaches, as well as their performance on various types of questions, such as those from MSMARCO and Jeopardy. The interaction between parametric and non-parametric memory is highlighted through an example involving a Hemingway question. We explore how the model retrieves relevant documents to generate an answer that may not be present in any single document but can be deduced by combining information from multiple sources. Finally, we touch upon the implications of RAG for hallucination control and improving factual accuracy in language generation tasks. Overall, this discussion provides valuable insights into the potential applications and benefits of RAG in various domains.
The ethical challenges associated with the development and implementation of artificial intelligence (AI) systems are becoming increasingly important as AI becomes pervasive in various aspects of life, such as healthcare, finance, education, and entertainment. Key areas of focus within AI ethics include bias and fairness, transparency, accountability, privacy, and security. Unethical AI practices can lead to discrimination, inequality, misinformation, manipulation, reinforcement of harmful stereotypes, and a lack of accountability. Practitioners have a responsibility to prioritize ethical considerations in their work, including mitigating bias in machine learning systems through diverse data collection, data preprocessing, fairness metrics evaluation, collaboration with stakeholders, AI ethics guidelines establishment, and promoting transparency and accountability. By focusing on these principles, AI developers can contribute to a more equitable and sustainable future.
Drag Your GAN is a novel approach for achieving precise control over the pose, shape, expression, and layout of objects generated by Generative Adversarial Networks (GANs). It allows users to "drag" any points of an image to specific target points, enabling deformation of images with better control over where pixels end up to produce ultra-realistic outputs. The method involves point-based manipulation and motion supervision, using feature maps from the generator's intermediate layers as discriminative features for motion supervision. The technique has been compared against state-of-the-art methods in point tracking and image manipulation, showing promising results. Potential applications include image editing, animation, and other creative tasks where precise control over object appearance is desired.
In this paper reading, LIMA (Less Is More for Alignment) demonstrates the efficiency and effectiveness of large language models through pre-training and minimal fine-tuning, outperforming its contemporaries in various evaluations, including human preference and GPT-4 comparisons. The research highlights the power of pre-training and the importance of data quality, diversifying the training data beyond just questions and online community sets to achieve better results. The findings suggest that input diversity and output quality have a significant impact on the performance of large language models, and that fine-tuning can be more effective than prompt engineering in certain cases. The paper also discusses the limitations of current methods and the need for further research on fine-tuning and alignment.
Cross Validation is a technique used in machine learning to evaluate the performance of predictive models by dividing the data into subsets, training the model on one subset, and testing it on another. This helps prevent overfitting and provides an unbiased estimate of the model's generalization error. There are various types of cross-validation techniques, including hold-out method, k-fold method, leave-p-out method, and rolling cross validation. Cross validation is especially important for large language models (LLMs) as it helps in tuning hyperparameters and ensuring that the model truly generalizes well to new examples.
Bias and fairness are crucial aspects to consider when developing machine learning models. Bias refers to systematic errors that arise due to discriminatory or unfair patterns in data, while fairness is the absence of prejudice or preference for an individual or group based on their characteristics. Sensitive groups, such as race, ethnicity, gender, age, religion, disability, and sexual orientation, are often the focus of fairness concerns in machine learning. Non-sensitive group bias occurs when a model consistently makes errors due to its inability to represent certain aspects of the data accurately. To address bias in machine learning models, it's important to identify the sources of bias and take steps to mitigate them. This can involve collecting more diverse and representative training data, selecting appropriate model architectures and algorithms, and using techniques such as regularization to prevent overfitting. It's also crucial to critically examine the assumptions and decisions made during the model-building process, and to involve diverse stakeholders in the development and evaluation of the model. Fairness metrics like recall parity, false positive rate parity, and disparate impact can help assess bias in machine learning models. The choice of fairness metric depends on the specific context and goals of the machine learning model being developed. Assessing bias for non-sensitive groups involves data analysis, model evaluation, and human review. The industry standard for evaluating fairness metric values is the four-fifths rule, which suggests a threshold between 0.8 and 1.25. The appropriate threshold for a fairness metric depends on factors such as acceptable levels of disparity, trade-offs with other performance metrics, and evaluation of multiple thresholds. To monitor fairness metrics for models in production, consider using tools like Arize to ensure that the models are fair, accurate, and aligned with values and goals.
MLflow is an open-source platform designed for the end-to-end machine learning lifecycle, providing a centralized location for storing and managing all machine learning models, data, and metadata about model experiments. It comprises four main components: Tracking, Registry, Models, and Projects, each catering to a specific aspect of the machine learning pipeline. MLflow improves collaboration among data scientists and MLOps teams by leveraging features such as version control, metadata management, and access control, streamlining the process of creating and using machine learning models. The platform also offers tools for tracking and logging experiments, packaging and deploying machine learning models, managing dependencies and reproducibility, and ensuring efficient model management throughout the lifecycle. MLflow Registry provides a centralized location for storing, managing, and sharing machine learning models, enabling version control and collaboration across different teams and organizations. Finally, MLflow Projects offers a standardized method for packaging and sharing code, data, and environments across machine learning workflows, facilitating seamless reproduction and collaboration on experiments.
Ivan Porollo, co-founder of Cerebral Valley, discusses the current state of AI and his vision for the community. He emphasizes the importance of knowledge sharing among builders and founders to drive innovation in AI and startups. Cerebral Valley hosts various events such as hackathons, technical workshops, and speaker series to engage with its members. The community is diverse, attracting individuals from different backgrounds who are interested in learning about or building applications using AI technology.
The motivation behind InstructGPT is to create a model that can perform useful cognitive tasks, such as summarizing news articles or writing stories, by leveraging reinforcement learning with human feedback (RLHF). The team at OpenAI aims to fine-tune the model on an objective function that optimizes its performance as a useful assistant. They use human data, including labelers who provide preferences over generated outputs, to train the reward model and then optimize the neural network to produce good outputs according to this representation. The method has shown promising results, but there are challenges in scaling up to more powerful language models, such as evaluating their behavior and mitigating potential misalignment issues. Researchers are exploring new approaches, including scalable supervision and interpretability techniques, to address these challenges and ensure that the models align with human values.
The Observe plugin is an OpenAI plugin that provides LLM observability, allowing users to analyze and understand their data. The plugin was built in under 24 hours by a team at Arize AI and consists of several components, including the API, manifest file, and OpenAPI specification. These components work together to provide a user-friendly interface for exploring and visualizing data, as well as generating summaries and clustering similar data points. The plugin uses GPT-4's chat completion API to perform tasks such as summarization and sentiment analysis, and can be integrated with existing APIs and applications. However, the team notes that there are challenges ahead, including improving context management practices and scaling the solution to larger clusters with longer text.
Survey: Massive Retooling Around Large Language Models Underway``` The adoption rate of large language models is high, with nearly one in ten machine learning teams already deploying LLM applications into production and nearly half planning to do so within a year. Data privacy and accuracy concerns are major barriers to deployment, highlighting the need for better observability tools. OpenAI dominates the field, with its models being considered or used by most ML teams. Prompt engineering is becoming increasingly popular, with emerging techniques like vector databases and agents also gaining traction. Despite potential risks, there is no pause on giant AI experiments, and machine learning teams are retooling around large foundational models to ensure readiness for deployment.
Getting a first role in the tech industry can be challenging, but with the right strategy, it's achievable. Amber, an experienced machine learning engineer, shares five rules to help new graduates and professionals navigate the transition into tech. The rules focus on making the most of networking opportunities, applying strategically, and preparing for interviews. By following these guidelines, individuals can increase their chances of landing a role in tech and building a strong foundation for future career growth.
Hungry Hungry Hippos (H3) and its creators, Dan Fu and Tri Dao, have developed a language modeling architecture that performs comparably to transformers while admitting much longer context length, making it suitable for tasks such as audio processing and biological applications. Their approach uses state space models, which are inspired by old concepts from control theory but have been adapted for deep learning. The H3 model achieves impressive results on large benchmark tests, often rivaling or surpassing transformer-based models. When combined with one or two attention layers, the blended architecture shows even more promising results. The researchers believe that state space methods could be more efficient during inference, which is a crucial concern for deploying these models in products. Applications of H3 include code generation, video processing, and biological applications, as well as interactive AI workflows and automatic slide generation. These new architectures will require interaction between users and the system, making long-range context increasingly important.
In this podcast, Timo Schick and Thomas Scialom from Meta AI discuss their research on Toolformer, a language model that can access external tools such as calculators and question-answer search APIs to generate more powerful and accurate output. They explain the limitations of current "vanilla" language models, which cannot access information about the external world, and how Toolformer aims to address these issues by equipping models with the ability to communicate via APIs or external tools. The researchers also share their thoughts on the future of tool-LLM powered products and potential areas of research in this field.
PCI DSS 4.0 is a global standard that provides a baseline of technical and operational requirements designed to protect account data, particularly in light of rising credit card fraud and identity theft. Arize AI has achieved this certification as a Level 1 Service Provider due to its potential access to or impact on the security of ingested credit card information, with the goal of safeguarding customers' data entrusted to it. The company pursued PCI DSS 4.0 Certification not by obligation but as a conscious choice to better protect and safeguard its customers' data, demonstrating compliant controls and mechanisms in place to properly safeguard data. Arize AI is committed to maintaining or exceeding the standards required by the PCI leadership as well as its own policies, with a focus on continually protecting and safeguarding customer data.
Zippi, a Brazil-based fintech company founded by MIT alumni, aims to provide affordable and accessible financial services to over 30 million micro entrepreneurs who face challenges accessing credit from traditional banks. The company leverages machine learning (ML) models to assess credit risk, price sensitivity, and limit sensitivity, helping customers achieve their business goals. Zippi's commitment to using cutting-edge technology and best practices in the market sets it apart as a fintech company. Arize is selected as the model monitoring and ML observability partner due to its strong support, effective onboarding process, and commitment to helping scale up skills for consistent leveraging of the tool.
A feature store is a central repository of precomputed features that serve as a single source of truth for machine learning projects, providing several benefits including centralized data management, clean data handling, shareable features across models, and standardized inference to the data. The adoption of feature stores has risen in popularity since Uber introduced the concept in 2017, with organizations utilizing them to streamline their data and ML lifecycle. Feature stores offer a one-stop-shop for data collection, transformation, and access, making it easier for teams to work together and reduce wasteful rework. By applying monitoring and quality checks to feature stores, practitioners can catch common machine learning issues such as missing values, data format changes, and statistical distribution shifts, ensuring better model performance and reduced latency.
Arize AI has been listed by Gartner for the second consecutive year in their Market Guide for AI Trust, Risk, and Security Management (AI TRiSM). The report highlights the growing need for infrastructure and processes to manage fairness, trust, risk, and security for AI systems. Arize offers an end-to-end ML observability platform that improves model performance and ensures ethical use of AI. Gartner emphasizes the importance of monitoring AI production data for drift, bias, attacks, and mistakes to achieve optimal AI performance and protect organizations from malicious attacks.
InstructGPT was one of the first major applications of reinforcement learning with human feedback to train large language models, it is the precursor to ChatGPT, and its creators are now discussing the future of aligning language models to human intention. The podcast episode features Long Ouyang and Ryan Lowe, scientists at OpenAI who developed InstructGPT, and explores the major ideas behind this breakthrough technology.
The World AI Conference, Data Science Salon Austin, NVIDIA GTC, Data Council, MLconf, KubeCon + CloudNativeCon (Europe), Arize:Observe, CDAO Spring, World Data Summit, NLPML, The Data Science Conference, ODSC Europe, Big Data + Analytics Summit Canada, MLCON, CVPR, Deep Learning World, VentureBeat Transform, ICML, Ai4, INTERSPEECH, Southern Data Science Conference, Coalesce, World Summit AI, Big Data & AI Toronto, TWIMLcon, ODSC EAST, Ray Summit, QCon, MLOps World, AI Dev World, KubeCon + CloudNativeCon (North America), ODSC WEST, Re:Work MLOps Summit, Toronto Machine Learning Summit, NeurIPS, The AI Summit New York, VOICE Summit, and Apply(Conf) are some of the top machine learning and data science conferences in 2023. These events offer a wide range of topics, including AI ethics, deep learning, natural language processing, computer vision, and more. They provide opportunities for networking, hands-on training sessions, workshops, and keynotes from industry leaders and experts. Many of these conferences are focused on practical applications and real-world use cases, making them attractive to professionals looking to learn new skills or stay up-to-date with the latest developments in the field.