Report Cards: Qualitative Evaluation of Language Models Using Natural
  Language Summaries
Authors: name: Blair Yang, name: Fuyang Cui, name: Keiran Paster, name: Jimmy Ba, name: Pashootan Vaezipoor, name: Silviu Pitis, name: Michael R. Zhang

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.