The paper "Lessons from the Trenches on Reproducible Evaluation of Language Models" addresses the challenges and best practices in evaluating large language models (LLMs). The authors, from various institutions and companies, draw on their collective experience to provide guidance and insights. They highlight common issues such as the sensitivity of models to evaluation setup, the difficulty of proper comparisons across methods, and the lack of reproducibility and transparency. The paper outlines best practices for addressing these challenges, including sharing exact prompts and code, avoiding copying results from other implementations, providing model outputs, performing qualitative analyses, and measuring and reporting uncertainty. The authors introduce the Language Model Evaluation Harness (Lm-eval), an open-source library designed to facilitate reproducible and extensible evaluation of LMs. Lm-eval aims to solve the orchestration problem by providing a standardized implementation of common tasks and allowing users to select their desired tasks and use cases. The library supports various evaluation metrics, statistical testing, and qualitative analysis, making it easier for researchers to conduct thorough evaluations. The paper also includes case studies demonstrating how Lm-eval can improve evaluation rigor and understanding. For example, it shows how prompt variations can significantly impact model performance and how different evaluation setups can affect scores. The authors emphasize the importance of sharing detailed evaluation setups and results to ensure reproducibility and confidence in evaluation findings. Overall, the paper provides valuable insights and practical tools for researchers and practitioners in the field of language model evaluation, aiming to enhance the robustness and reliability of LLM evaluations.The paper "Lessons from the Trenches on Reproducible Evaluation of Language Models" addresses the challenges and best practices in evaluating large language models (LLMs). The authors, from various institutions and companies, draw on their collective experience to provide guidance and insights. They highlight common issues such as the sensitivity of models to evaluation setup, the difficulty of proper comparisons across methods, and the lack of reproducibility and transparency. The paper outlines best practices for addressing these challenges, including sharing exact prompts and code, avoiding copying results from other implementations, providing model outputs, performing qualitative analyses, and measuring and reporting uncertainty. The authors introduce the Language Model Evaluation Harness (Lm-eval), an open-source library designed to facilitate reproducible and extensible evaluation of LMs. Lm-eval aims to solve the orchestration problem by providing a standardized implementation of common tasks and allowing users to select their desired tasks and use cases. The library supports various evaluation metrics, statistical testing, and qualitative analysis, making it easier for researchers to conduct thorough evaluations. The paper also includes case studies demonstrating how Lm-eval can improve evaluation rigor and understanding. For example, it shows how prompt variations can significantly impact model performance and how different evaluation setups can affect scores. The authors emphasize the importance of sharing detailed evaluation setups and results to ensure reproducibility and confidence in evaluation findings. Overall, the paper provides valuable insights and practical tools for researchers and practitioners in the field of language model evaluation, aiming to enhance the robustness and reliability of LLM evaluations.