--- title: "Who cites LexEval: A Comprehensive — 63 citing papers" description: "63 papers of the Elamur Research library cite «LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language…" canonical: "https://research.elamur.ai/free/en/papers/li-2024-lexeval-comprehensive-chinese-legal-benchmark/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/li-2024-lexeval-comprehensive-chinese-legal-benchmark/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LexEval: A Comprehensive Chinese Legal Benchmark…](https://md.research.elamur.ai/free/en/papers/li-2024-lexeval-comprehensive-chinese-legal-benchmark/) / cited by # Who cites «LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models» 63 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models»](https://md.research.elamur.ai/free/en/papers/li-2024-lexeval-comprehensive-chinese-legal-benchmark/) 1. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods arXiv:2412.05579**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/) 2. **[LegalAgentBench: Evaluating LLM Agents in Legal Domain](https://md.research.elamur.ai/free/en/papers/li-2025-legalagentbench-evaluating-llm-agents-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haitao Li, Junjie Chen, Jingli Yang, et al.*2025 · 62 citations* 3. **[LLM Agents in Law: Taxonomy, Applications, and Challenges](https://md.research.elamur.ai/free/en/papers/liu-2026-llm-agents-law-taxonomy-applications/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuang Liu, Ruijia Zhang, Ruoyun Ma, et al.*2026 · 4 citations* 4. **[Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs' Legal Reasoning Capabilities](https://md.research.elamur.ai/free/en/papers/oh-2026-korean-canonical-legal-benchmark-knowledge/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hongseok Oh, Wonseok Hwang, Kyoung-Woon On*2026 · 3 citations* 5. **[LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation](https://md.research.elamur.ai/free/en/papers/yuan-2026-lecode-benchmark-dataset-interactive-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weikang Yuan, Kaisong Song, Zhuoren Jiang, et al.*2026 · 3 citations* 6. **[LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence](https://md.research.elamur.ai/free/en/papers/liu-2026-lexgenius-expert-level-benchmark-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenjin Liu, Haoran Luo, Xin Feng, et al.*2026 · 2 citations* 7. [**BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law arXiv:2605.28183**Sebastian Nagl, Ann-Kristin Mayrhofer, Martin Heidebach, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nagl-2026-benger-benchmarking-llm-systems-subsumption/) 8. **[DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis](https://md.research.elamur.ai/free/en/papers/shi-2026-dataarc-syndata-toolkit-unified-closed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhichao Shi, Cehao Yang, Hao Zhou, et al.*2026* 9. **[Prompt Optimization for Relation Extraction using Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2026-prompt-optimization-relation-extraction-reinforcement/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ying Liu, Dong Shuai, Cui Zibo, et al.*2026* 10. **[Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice](https://md.research.elamur.ai/free/en/papers/wang-2026-ip-intelligence-benchmarking-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiyao Wang, Guhong Chen, Hongbo Wang, et al.*2026* 53 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/li-2024-lexeval-comprehensive-chinese-legal-benchmark/) — LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models