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Lessons from the Trenches on Reproducible Evaluation of Language Models

๐ŸŒˆ Abstract

The article discusses the challenges in evaluating language models and provides recommendations to improve the rigor and reproducibility of such evaluations. It introduces the Language Model Evaluation Harness (lm-eval), an open-source library for reproducible evaluation of language models.

๐Ÿ™‹ Q&A

[01] Challenges in Evaluating Language Models

1. What are the key challenges in evaluating language models?

  • The "Key Problem": There can be many semantically equivalent but syntactically different ways of expressing the same idea, making it difficult to automatically detect when two sentences convey the same content.
  • The high cost and limitations of relying solely on human evaluation.
  • The use of automated metrics like BLEU and ROUGE, which have flaws and reproducibility challenges.
  • The sensitivity of language models to minor implementation details that are often not reported.
  • The lack of agreement on how to draw fair comparisons across models and methods.
  • The difficulty in comparing to prior work due to models being unavailable or deprecated.
  • The fast-changing progress and conventions in the field, with many benchmarks not designed for the current paradigm of language models.

2. How do these challenges impact the evaluation of language models?

  • They can lead to skewed performance comparisons and influence the direction of future research.
  • They can result in the deployment of suboptimal or harmful models on tasks they are ill-suited for.
  • They make it extremely difficult to ensure fair comparisons across works, even when evaluating on the same benchmark.

[02] Best Practices for Language Model Evaluation

1. What are the recommended best practices for improving language model evaluation?

  • Always share the exact prompts and evaluation code used.
  • Avoid copying results from other implementations, as comparing results across papers can be misleading.
  • Provide model outputs alongside evaluation code to allow for recalculation of scores and statistical analysis.
  • Perform qualitative analyses by reviewing a small batch of results before testing at scale.
  • Perform statistical significance testing to boost the reliability of claimed results.

[03] The Language Model Evaluation Harness

1. What is the purpose of the Language Model Evaluation Harness (lm-eval)? The lm-eval library aims to solve the orchestration problem in language model evaluation by:

  • Providing a standardized implementation of many common evaluation tasks.
  • Allowing for the easy integration of novel language model implementations.
  • Enabling reproducible evaluation by versioning task implementations and providing support for qualitative analysis and statistical testing.

2. How does lm-eval address the challenges in language model evaluation?

  • By providing standardized task implementations, lm-eval encourages reproducible evaluation and fair comparisons across models.
  • The library's support for qualitative analysis, statistical testing, and output sharing helps improve the rigor of evaluations.
  • The extensible design of lm-eval allows the community to easily contribute new evaluation tasks and use cases, fostering a more robust evaluation ecosystem.

[04] Case Studies

1. How has lm-eval been used to study the sensitivity of language models to prompts? The article presents a case study where lm-eval was used to compare the performance of language models on the ARC and MMLU benchmarks using different prompting styles. The results showed that the choice of prompting style can significantly impact the relative performance of the models, highlighting the importance of reporting full evaluation details.

2. How has lm-eval empowered benchmark creators and LM evaluation research? The article describes how lm-eval has been adopted by the community to make the design and prototyping of new evaluation benchmarks easier. The extensible task configuration and low-friction contribution process have allowed researchers to directly contribute their new evaluation tasks to lm-eval, improving the dissemination and recognition of these benchmarks.

Additionally, lm-eval has been used to explore the effects of prompting and other factors on model robustness and performance, as well as to investigate the tradeoffs between different evaluation methodologies, such as loglikelihood versus generative evaluation.

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