Lessons from the Trenches on Reproducible Evaluation of Language Models

TL;DR

This paper discusses the challenges in evaluating large language models, offers best practices, and introduces an open-source evaluation library to improve reproducibility and transparency in NLP research.

Contribution

It provides a comprehensive overview of evaluation challenges, best practices, and introduces the lm-eval library for reproducible language model assessment.

Findings

Identification of key evaluation challenges

Introduction of the lm-eval library

Case studies demonstrating improved evaluation practices

Abstract

Effective evaluation of language models remains an open challenge in NLP. Researchers and engineers face methodological issues such as the sensitivity of models to evaluation setup, difficulty of proper comparisons across methods, and the lack of reproducibility and transparency. In this paper we draw on three years of experience in evaluating large language models to provide guidance and lessons for researchers. First, we provide an overview of common challenges faced in language model evaluation. Second, we delineate best practices for addressing or lessening the impact of these challenges on research. Third, we present the Language Model Evaluation Harness (lm-eval): an open source library for independent, reproducible, and extensible evaluation of language models that seeks to address these issues. We describe the features of the library as well as case studies in which the library has been used to alleviate these methodological concerns.