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Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries

Blair Yang, Fuyang Cui, Keiran Paster, Jimmy Ba, Pashootan Vaezipoor, Silviu Pitis, Michael R. Zhang

TL;DR

Through experimentation with popular LLMs, it is demonstrated that report cards provide insights beyond traditional benchmarks and can help address the need for a more interpretable and holistic evaluation of LLMs.

Abstract

The rapid development and dynamic nature of large language models (LLMs) make it difficult for conventional quantitative benchmarks to accurately assess their capabilities. We propose report cards, which are human-interpretable, natural language summaries of model behavior for specific skills or topics. We develop a framework to evaluate report cards based on three criteria: specificity (ability to distinguish between models), faithfulness (accurate representation of model capabilities), and interpretability (clarity and relevance to humans). We also propose an iterative algorithm for generating report cards without human supervision and explore its efficacy by ablating various design choices. Through experimentation with popular LLMs, we demonstrate that report cards provide insights beyond traditional benchmarks and can help address the need for a more interpretable and holistic evaluation of LLMs.

Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries

TL;DR

Through experimentation with popular LLMs, it is demonstrated that report cards provide insights beyond traditional benchmarks and can help address the need for a more interpretable and holistic evaluation of LLMs.

Abstract

The rapid development and dynamic nature of large language models (LLMs) make it difficult for conventional quantitative benchmarks to accurately assess their capabilities. We propose report cards, which are human-interpretable, natural language summaries of model behavior for specific skills or topics. We develop a framework to evaluate report cards based on three criteria: specificity (ability to distinguish between models), faithfulness (accurate representation of model capabilities), and interpretability (clarity and relevance to humans). We also propose an iterative algorithm for generating report cards without human supervision and explore its efficacy by ablating various design choices. Through experimentation with popular LLMs, we demonstrate that report cards provide insights beyond traditional benchmarks and can help address the need for a more interpretable and holistic evaluation of LLMs.
Paper Structure (63 sections, 2 equations, 16 figures, 9 tables, 2 algorithms)

This paper contains 63 sections, 2 equations, 16 figures, 9 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Method
  3. The Role of Qualitative Evaluation
  4. Quantitative Metrics for Evaluating Report Cards
  5. Contrastive accuracy
  6. Card Elo
  7. Human scoring
  8. Generating Report Cards
  9. Experiments
  10. Setup
  11. Topics
  12. Models
  13. Contrastive Evaluation
  14. Faithfulness Evaluation
  15. Human Scoring
  16. ...and 48 more sections

Figures (16)

  • Figure 1: Example excerpts from Report Cards, which provide an overview of the model's strengths and weaknesses in their respective domains. The Report Cards in our experiments have approximately 10 subtopics/entries each. Complete samples can be found on our website.
  • Figure 2: A contrastive guess round.
  • Figure 3: One step of PRESS (Alg. \ref{['alg:press']})
  • Figure 4: Solid bars: de-stylized performance; transparent bars: original performance. Report Cards maintain the best performance when stylistic features are removed.
  • Figure 5: $R^2$ faithfulness scores for Card Elo, Arena Elo, and Few-shot Elo (with and without aggregation). For Few-shot Elo, each point represents one realization of a few-shot. The red label indicates the improvement of $R^2$ from aggregation compared to the mean. Our Card Elo has the strongest correlation.
  • ...and 11 more figures