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相关论文: LexEval: A Comprehensive Chinese Legal Benchmark f…

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Large language models (LLMs) have demonstrated remarkable capabilities across various applications, highlighting the urgent need for comprehensive safety evaluations. In particular, the enhanced Chinese language proficiency of LLMs,…

计算与语言 · 计算机科学 2025-02-27 Shuyi Liu , Simiao Cui , Haoran Bu , Yuming Shang , Xi Zhang

Large language models (LLMs) have demonstrated remarkable advances in mathematical and logical reasoning, yet statistics, as a distinct and integrative discipline, remains underexplored in benchmarking efforts. To address this gap, we…

Legal relations serve as an important analytical framework for dispute resolution in civil cases. However, legal relations in Chinese civil cases remain underexplored in the field of legal AI, largely due to the absence of comprehensive…

计算与语言 · 计算机科学 2026-04-21 Yida Cai , Ranjuexiao Hu , Huiyuan Xie , Chenyang Li , Yun Liu , Yuxiao Ye , Zhenghao Liu , Weixing Shen , Zhiyuan Liu

As large language models (LLMs) are increasingly used in legal applications, current evaluation benchmarks tend to focus mainly on factual accuracy while largely neglecting important linguistic quality aspects such as clarity, coherence,…

计算与语言 · 计算机科学 2025-11-11 Li yunhan , Wu gengshen

Large language models (LLMs) are possessed of numerous beneficial capabilities, yet their potential inclination harbors unpredictable risks that may materialize in the future. We hence propose CRiskEval, a Chinese dataset meticulously…

计算与语言 · 计算机科学 2024-06-10 Ling Shi , Deyi Xiong

Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks. Nonetheless, when it comes to practical Chinese legal tasks, these…

计算与语言 · 计算机科学 2024-06-10 Zhi Zhou , Jiang-Xin Shi , Peng-Xiao Song , Xiao-Wen Yang , Yi-Xuan Jin , Lan-Zhe Guo , Yu-Feng Li

Large language models (LLMs) have shown impressive capabilities across various natural language tasks. However, evaluating their alignment with human preferences remains a challenge. To this end, we propose a comprehensive human evaluation…

Large language models (LLMs) have shown significant promise across various medical applications, with ophthalmology being a notable area of focus. Many ophthalmic tasks have shown substantial improvement through the integration of LLMs.…

计算与语言 · 计算机科学 2025-02-04 Chengfeng Zhou , Ji Wang , Juanjuan Qin , Yining Wang , Ling Sun , Weiwei Dai

Long-form legal reasoning remains a key challenge for large language models (LLMs) in spite of recent advances in test-time scaling. To address this, we introduce LEXam, a novel benchmark derived from 340 law exams spanning 116 law school…

The rapid development of Large Language Models (LLMs) in vertical domains, including intellectual property (IP), lacks a specific evaluation benchmark for assessing their understanding, application, and reasoning abilities. To fill this…

计算与语言 · 计算机科学 2024-06-19 Qiyao Wang , Jianguo Huang , Shule Lu , Yuan Lin , Kan Xu , Liang Yang , Hongfei Lin

With the increasing intelligence and autonomy of LLM agents, their potential applications in the legal domain are becoming increasingly apparent. However, existing general-domain benchmarks cannot fully capture the complexity and subtle…

计算与语言 · 计算机科学 2024-12-24 Haitao Li , Junjie Chen , Jingli Yang , Qingyao Ai , Wei Jia , Youfeng Liu , Kai Lin , Yueyue Wu , Guozhi Yuan , Yiran Hu , Wuyue Wang , Yiqun Liu , Minlie Huang

Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants. However, the effective evaluation of alignment for emerging Chinese LLMs is still largely unexplored. To fill in this gap,…

With the rapid evolution of large language models (LLMs), there is a growing concern that they may pose risks or have negative social impacts. Therefore, evaluation of human values alignment is becoming increasingly important. Previous work…

In this report, we introduce our first-generation reasoning model, LexPro-1.0, a large language model designed for the highly specialized Chinese legal domain, offering comprehensive capabilities to meet diverse realistic needs. Existing…

计算与语言 · 计算机科学 2025-03-12 Haotian Chen , Yanyu Xu , Boyan Wang , Chaoyue Zhao , Xiaoyu Han , Fang Wang , Lizhen Cui , Yonghui Xu

With the rapid development of large language models (LLMs), assessing their performance on health-related inquiries has become increasingly essential. The use of these models in real-world contexts-where misinformation can lead to serious…

计算与语言 · 计算机科学 2025-02-24 Chenlu Guo , Nuo Xu , Yi Chang , Yuan Wu

Large language models (LLMs) are being increasingly integrated into legal applications, including judicial decision support, legal practice assistance, and public-facing legal services. While LLMs show strong potential in handling legal…

Given the importance of ancient Chinese in capturing the essence of rich historical and cultural heritage, the rapid advancements in Large Language Models (LLMs) necessitate benchmarks that can effectively evaluate their understanding of…

计算与语言 · 计算机科学 2024-03-12 Yuting Wei , Yuanxing Xu , Xinru Wei , Simin Yang , Yangfu Zhu , Yuqing Li , Di Liu , Bin Wu

The potential of large language models (LLMs) in specialized domains such as legal risk analysis remains underexplored. In response to growing interest in locally deploying open-source LLMs for legal tasks while preserving data…

人工智能 · 计算机科学 2025-08-06 Shuang Liu , Zelong Li , Ruoyun Ma , Haiyan Zhao , Mengnan Du

With the continuous emergence of Chinese Large Language Models (LLMs), how to evaluate a model's capabilities has become an increasingly significant issue. The absence of a comprehensive Chinese benchmark that thoroughly assesses a model's…

The emergence of various medical large language models (LLMs) in the medical domain has highlighted the need for unified evaluation standards, as manual evaluation of LLMs proves to be time-consuming and labor-intensive. To address this…

计算与语言 · 计算机科学 2023-12-21 Yan Cai , Linlin Wang , Ye Wang , Gerard de Melo , Ya Zhang , Yanfeng Wang , Liang He