Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries
This work addresses the need for more interpretable and holistic evaluation methods for LLMs, which is crucial for researchers and practitioners dealing with rapid model development, though it is incremental in nature.
The authors tackled the problem of evaluating large language models (LLMs) by proposing 'report cards'—natural language summaries of model behavior—to address limitations of conventional quantitative benchmarks. They demonstrated that these report cards provide insights beyond traditional benchmarks, offering a more interpretable and holistic evaluation.
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.