LLM Benchmarks: Guide to Evaluating Language Models

The article discusses the importance of language model benchmarks (LLMs) in evaluating AI performance, particularly large language models like GPT-4. These benchmarks provide an objective measure for developers and users to compare competing models based on their ability to complete specific natural language processing tasks. They also offer valuable insights into areas where a model excels or struggles, helping researchers gauge the current state of the art in AI research. The history of AI and LLM benchmarks is traced back to early machine translation systems in the 1960s-70s, followed by bag-of-words models in the 1980s-90s, sequence models and named entity recognition in the early 2000s, word embeddings in the mid-2010s, attention models and question answering in the late 2010s, and finally, GLUE and SuperGLUE benchmarks. The article also highlights some emerging trends in LLM benchmarking, such as a focus on ethical aspects like fairness and bias, explainability, and expanding capabilities beyond basic NLP tasks. The author emphasizes that no single test can fully capture an LLM's wide array of abilities and potential weaknesses, making comprehensive benchmarking crucial for understanding these complex AI systems. The article concludes by providing a list of Deepgram articles covering various LLM benchmarks, with plans to update the list as new benchmarks emerge.