The paper discusses the challenges and best practices for evaluating large language models (LLMs), emphasizing the need for reproducible, transparent, and fair evaluation methods. It highlights key issues such as the difficulty of assessing natural language responses, benchmark design, and implementation variability. The authors propose the Language Model Evaluation Harness (lm-eval), an open-source library designed to enable consistent, reproducible, and extensible evaluation of LLMs. The library addresses challenges like the "Key Problem" of evaluating semantically equivalent responses, benchmark validity, and implementation differences. It also provides tools for qualitative analysis, statistical testing, and sharing evaluation details to improve reproducibility and fairness. The paper outlines case studies demonstrating lm-eval's utility in improving evaluation rigor, including multiprompt evaluations and comparisons across different benchmark setups. The authors emphasize the importance of sharing exact prompts, code, and evaluation details to ensure fair comparisons and robust results. The library supports various evaluation tasks, including conditional loglikelihoods, perplexities, and generation-based requests, and is designed to be flexible and user-friendly for researchers and developers. The paper concludes by advocating for the use of lm-eval to enhance the evaluation ecosystem and promote better communication of evaluation practices.The paper discusses the challenges and best practices for evaluating large language models (LLMs), emphasizing the need for reproducible, transparent, and fair evaluation methods. It highlights key issues such as the difficulty of assessing natural language responses, benchmark design, and implementation variability. The authors propose the Language Model Evaluation Harness (lm-eval), an open-source library designed to enable consistent, reproducible, and extensible evaluation of LLMs. The library addresses challenges like the "Key Problem" of evaluating semantically equivalent responses, benchmark validity, and implementation differences. It also provides tools for qualitative analysis, statistical testing, and sharing evaluation details to improve reproducibility and fairness. The paper outlines case studies demonstrating lm-eval's utility in improving evaluation rigor, including multiprompt evaluations and comparisons across different benchmark setups. The authors emphasize the importance of sharing exact prompts, code, and evaluation details to ensure fair comparisons and robust results. The library supports various evaluation tasks, including conditional loglikelihoods, perplexities, and generation-based requests, and is designed to be flexible and user-friendly for researchers and developers. The paper concludes by advocating for the use of lm-eval to enhance the evaluation ecosystem and promote better communication of evaluation practices.