Zhiwei Fei


2024

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LawBench: Benchmarking Legal Knowledge of Large Language Models
Zhiwei Fei | Xiaoyu Shen | Dawei Zhu | Fengzhe Zhou | Zhuo Han | Alan Huang | Songyang Zhang | Kai Chen | Zhixin Yin | Zongwen Shen | Jidong Ge | Vincent Ng
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

We present LawBench, the first evaluation benchmark composed of 20 tasks aimed to assess the ability of Large Language Models (LLMs) to perform Chinese legal-related tasks. LawBench is meticulously crafted to enable precise assessment of LLMs’ legal capabilities from three cognitive levels that correspond to the widely accepted Bloom’s cognitive taxonomy. Using LawBench, we present a comprehensive evaluation of 21 popular LLMs and the first comparative analysis of the empirical results in order to reveal their relative strengths and weaknesses. All data, model predictions and evaluation code are accessible from https://github.com/open-compass/LawBench.

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LJPCheck: Functional Tests for Legal Judgment Prediction
Yuan Zhang | Wanhong Huang | Yi Feng | Chuanyi Li | Zhiwei Fei | Jidong Ge | Bin Luo | Vincent Ng
Findings of the Association for Computational Linguistics: ACL 2024

Legal Judgment Prediction (LJP) refers to the task of automatically predicting judgment results (e.g., charges, law articles and term of penalty) given the fact description of cases. While SOTA models have achieved high accuracy and F1 scores on public datasets, existing datasets fail to evaluate specific aspects of these models (e.g., legal fairness, which significantly impact their applications in real scenarios). Inspired by functional testing in software engineering, we introduce LJPCHECK, a suite of functional tests for LJP models, to comprehend LJP models’ behaviors and offer diagnostic insights. We illustrate the utility of LJPCHECK on five SOTA LJP models. Extensive experiments reveal vulnerabilities in these models, prompting an in-depth discussion into the underlying reasons of their shortcomings.

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MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark
Hongwei Liu | Zilong Zheng | Yuxuan Qiao | Haodong Duan | Zhiwei Fei | Fengzhe Zhou | Wenwei Zhang | Songyang Zhang | Dahua Lin | Kai Chen
Findings of the Association for Computational Linguistics: ACL 2024

Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional perspective, which fall short in providing a holistic assessment of the LLMs’ math capabilities. To address this gap, we introduce MathBench, a new benchmark that rigorously assesses the mathematical capabilities of large language models. MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills. The benchmark progresses through five distinct stages, from basic arithmetic to college mathematics, and is structured to evaluate models at various depths of knowledge. Each stage includes theoretical questions and application problems, allowing us to measure a model’s mathematical proficiency and its ability to apply concepts in practical scenarios. MathBench aims to enhance the evaluation of LLMs’ mathematical abilities, providing a nuanced view of their knowledge understanding levels and problem solving skills in a bilingual context.