Report Cards: Qualitative Evaluation of Language Models Using Natural
  Language Summaries
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.
66d8b48d8c67ac5bdf0056d8
Blair Yang
claimed_verified
2024-09-05T07:24:13.084Z
64dcdecfe0a1a37596576e46
/avatars/3e57d706108d92a4b3c5ee4e2b20398c.svg
Blair Yang
user
loveblairsky
66d8b48d8c67ac5bdf0056d9
Fuyang Cui
claimed_verified
2024-09-06T16:16:38.565Z
66db0da2947b2e8abc3f93dd
/avatars/9a8cdc10071329ce178c05380b90cc17.svg
Fuyang Cui
user
scottcfy
66d8b48d8c67ac5bdf0056da
Keiran Paster
66d8b48d8c67ac5bdf0056db
Jimmy Ba
66d8b48d8c67ac5bdf0056dc
Pashootan Vaezipoor
claimed_verified
2024-09-06T13:34:13.198Z
647630d4288784ce9ab9c2e3
/avatars/7ff4a595a2a2e4fccd8319a8cd8f236f.svg
Pashootan V
user
PashaV
66d8b48d8c67ac5bdf0056dd
Silviu Pitis
admin_assigned
2024-09-06T09:54:02.283Z
62d1cf75c40bde8b986e1c84
/avatars/4cf2b2557c37e65662250fc83fc83ab0.svg
Silviu Pitis
user
spitis
66d8b48d8c67ac5bdf0056de
Michael R. Zhang
Report Cards: Qualitative Evaluation of Language Models Using Natural
  Language Summaries
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 offer a human-interpretable evaluation framework for large language models, addressing limitations of traditional benchmarks.
large language models report cards specificity faithfulness interpretability iterative algorithm evaluation framework