Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries (2409.00844v1)

Abstract: The rapid development and dynamic nature of 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.

• The paper introduces Report Cards and the PRESS algorithm to iteratively generate natural language summaries capturing specific LLM capabilities.
• The approach outperforms one-shot methods by enhancing specificity, faithfulness, and interpretability through both quantitative and human evaluations.
• Experimental results reveal that Report Cards provide nuanced insights into model strengths and weaknesses, informing safer and more robust LLM deployment.

Qualitative Evaluation of LLMs Using Report Cards

The paper addresses the challenges associated with evaluating the capabilities of LLMs through conventional quantitative benchmarks. Traditional metrics often fail to provide a full picture of a model’s abilities and biases. The proposed solution—termed "Report Cards"—aims to generate human-interpretable, qualitative summaries of LLM behavior that encapsulate specific skills or topics.

Introduction

The vast and varied potential application space of LLMs makes comprehensive evaluation difficult. Existing benchmarks, such as GLUE and BIG-bench, largely rely on quantitative metrics, which risk overfitting and often do not capture nuanced model behaviors. The black-box nature of many LLMs further complicates understanding their capabilities. Thus, the need for more holistic and interpretable evaluations becomes evident.

Methodology

The authors propose a novel approach for LLM evaluation by generating "Report Cards," which are natural language summaries capturing model performance in specific areas. These Report Cards are assessed based on three main criteria:

1. Specificity: The ability to distinguish between different models.
2. Faithfulness: Accurate representation of model capabilities.
3. Interpretability: Clarity and relevance to human understanding.

The method involves an iterative process of summarization, outlined in the proposed iterative PRESS algorithm (Progressive Refinement for Effective Skill Summarization). This approach contrasts with a one-pass summarization where the entire dataset is considered at once, which results in overly general summaries.

Experimental Setup

Models and Datasets

The experiments use diverse models, including GPT-4o, GPT-3.5 Turbo, Claude 3.5 Sonnet, and several Llama and Mistral models. A variety of datasets are leveraged, including MMLU for academic topics, the Anthropic Advanced AI Risk dataset for evaluating ethical and safety compliance, and an internal Chinese grammar correction dataset.

Evaluation Metrics

The evaluation framework comprises three main components:

1. Contrastive Accuracy: Measures how well Report Cards can distinguish between models given their answers to specific questions.
2. Card Elo: Derives Elo scores from pairwise comparisons of Report Cards, correlating them with ground-truth ratings to measure faithfulness.
3. Human Scoring: Collects human ratings of relevance, informativeness, and clarity of Report Cards to assess interpretability.

Results

The experiments demonstrate that the PRESS algorithm outperforms baseline methods in creating specific and faithful Report Cards. Notably, the iterative refinement in PRESS improves Report Card quality over time, as evidenced by both quantitative measures (contrastive accuracy and faithfulness) and qualitative human evaluations.

Contrastive Evaluation

Report Cards generated via PRESS exhibit higher contrastive accuracy compared to few-shot baselines, indicating better specificity. Paraphrasing experiments further validate that Report Cards maintain contrastive power even when completions are stylistically altered, proving their robustness.

Card Elo and Faithfulness

Report Cards achieve high faithfulness scores, with strong correlation ($R^2$) between Card Elo and Ground-truth Elo ratings across both MMLU and Chinese grammar datasets. Report Cards provide a more faithful representation of model capabilities than generic quantitative metrics such as ChatbotArena Elo.

Human Scoring

Human evaluations indicate that Report Cards are generally rated highly for relevance, informativeness, and clarity. Preliminary investigations also show moderate alignment between LLM and human ratings, suggesting potential for automating this evaluation in the future.

Qualitative Examples

Examples illustrate how Report Cards effectively capture models’ strengths and weaknesses. For instance, Llama-3-8B-Instruct’s misunderstanding of combinatorial principles and Claude 3.5 Sonnet’s strong ethical adherence are accurately reflected in their respective Report Cards, providing nuanced insights that quantitative metrics may overlook.

Implications and Future Work

The introduction of Report Cards represents a significant step toward more interpretable and comprehensive evaluations of LLMs. By providing insights into specific capabilities and behaviors, these qualitative summaries can inform both the development and deployment of LLMs in various applications.

Future work should focus on expanding the types of tasks and domains for which Report Cards are applied. Additionally, improving the alignment between LLM-generated scores and human ratings will enhance the reliability and automation of this evaluation method. As LLM capabilities evolve, so too should the frameworks for their assessment, ensuring holistic and transparent evaluations.

Conclusion

Report Cards fill a critical gap left by traditional quantitative benchmarks, offering human-interpretable, detailed insights into LLM performance. Through innovative methods such as the PRESS algorithm, this approach balances specificity, faithfulness, and interpretability, paving the way for more informed and safer use of LLMs in diverse contexts.