  Large language models (LLMs) have demonstrated strong capabilities in various
aspects. However, when applying them to the highly specialized, safe-critical
legal domain, it is unclear how much legal knowledge they possess and whether
they can reliably perform legal-related tasks. To address this gap, we propose
a comprehensive evaluation benchmark LawBench. LawBench has been meticulously
crafted to have precise assessment of the LLMs' legal capabilities from three
cognitive levels: (1) Legal knowledge memorization: whether LLMs can memorize
needed legal concepts, articles and facts; (2) Legal knowledge understanding:
whether LLMs can comprehend entities, events and relationships within legal
text; (3) Legal knowledge applying: whether LLMs can properly utilize their
legal knowledge and make necessary reasoning steps to solve realistic legal
tasks. LawBench contains 20 diverse tasks covering 5 task types: single-label
classification (SLC), multi-label classification (MLC), regression, extraction
and generation. We perform extensive evaluations of 51 LLMs on LawBench,
including 20 multilingual LLMs, 22 Chinese-oriented LLMs and 9 legal specific
LLMs. The results show that GPT-4 remains the best-performing LLM in the legal
domain, surpassing the others by a significant margin. While fine-tuning LLMs
on legal specific text brings certain improvements, we are still a long way
from obtaining usable and reliable LLMs in legal tasks. All data, model
predictions and evaluation code are released in
https://github.com/open-compass/LawBench/. We hope this benchmark provides
in-depth understanding of the LLMs' domain-specified capabilities and speed up
the development of LLMs in the legal domain.
