Let's talk about LLM evaluation

The text explores the evaluation methods for Large Language Models (LLMs), highlighting the three primary approaches: automated benchmarking, human evaluation, and model-as-judge assessment. Automated benchmarking is useful for well-defined tasks but presents challenges such as contamination and the difficulty of evaluating broad capabilities. Human evaluations, while flexible and aligned with human preferences, are susceptible to biases and can be expensive to conduct systematically. Using models as judges can mitigate costs but introduces subtle biases, particularly when models favor their own outputs. The text also discusses the purposes of LLM evaluation, such as non-regression testing, model rankings, and assessing model capabilities, while acknowledging the limitations and infancy of the field. The author suggests that interdisciplinary approaches may enhance evaluation methods and emphasizes the importance of continuing to refine these techniques. The text concludes with an acknowledgment of various contributors and collaborators in the field.