--- title: "Who cites Hai Ye — 191 papers of our library — Elamur Research" description: "191 papers of the Elamur Research library cite the works of Hai Ye, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/hai-ye/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/hai-ye/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Hai Ye](https://md.research.elamur.ai/free/en/authors/hai-ye/) / cited by # Who cites Hai Ye 200 citations of their works from 191 papers of our library (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · [← Hai Ye](https://md.research.elamur.ai/free/en/authors/hai-ye/) 1. **[Neural Legal Judgment Prediction in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-neural-legal-judgment-prediction-english/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras*2019 · 559 citations* 2. **[How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations* 3. **[Legal Judgment Prediction via Topological Learning](https://md.research.elamur.ai/free/en/papers/zhong-2018-legal-judgment-prediction-topological-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Haoxi Zhong, Zhipeng Guo, Cunchao Tu, et al.*2018 · 480 citations* 4. **[Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://md.research.elamur.ai/free/en/papers/xiao-2021-lawformer-pre-trained-language-model/) [AI Open](https://md.research.elamur.ai/free/en/venues/ai-open/)**Chaojun Xiao, Xueyu Hu, Zhiyuan Liu, et al.*2021 · 396 citations* 5. [**LexGLUE: A Benchmark Dataset for Legal Language Understanding in English arXiv:2110.00976**Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras*2021 · 258 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/) 6. **[ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://md.research.elamur.ai/free/en/papers/malik-2021-ildc-cjpe-indian-legal-documents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, et al.*2021 · 251 citations* 7. **[LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2022-lexglue-benchmark-dataset-legal-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Abhik Jana, Dirk Hartung, et al.*2022 · 239 citations* 8. **[UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning](https://md.research.elamur.ai/free/en/papers/mao-2022-unipelt-unified-framework-parameter-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuning Mao, Lambert Mathias, Rui Hou, et al.*2022 · 165 citations* 9. [**Is GPT-3 a Good Data Annotator? arXiv:2212.10450**Bosheng Ding, Chengwei Qin, Linlin Liu, et al.*2022 · 147 citations*](https://md.research.elamur.ai/free/en/papers/ding-2022-gpt-3-good-data-annotator/) 10. **[Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics](https://md.research.elamur.ai/free/en/papers/bhargava-2021-generalization-nli-ways-not-go/) [Workshop on Insights from Negative Results in NLP](https://md.research.elamur.ai/free/en/venues/insights/)**Prajjwal Bhargava, Aleksandr Drozd, Anna Rogers*2021 · 131 citations* 11. [**DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services arXiv:2309.11325**Shengbin Yue, Wei Chen, Siyuan Wang, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/yue-2023-disc-lawllm-fine-tuning-large/) 12. **[Absolute Zero: Reinforced Self-play Reasoning with Zero Data](https://md.research.elamur.ai/free/en/papers/zhao-2025-absolute-zero-reinforced-self-play/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Andrew Zhao, Yiran Wu, Yang Yue, et al.*2025 · 57 citations* 13. **[WebDancer: Towards Autonomous Information Seeking Agency](https://md.research.elamur.ai/free/en/papers/wu-2025-webdancer-towards-autonomous-information-seeking/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jialong Wu, Baixuan Li, Runnan Fang, et al.*2025 · 50 citations* 14. [**One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support arXiv:2306.09237**Ronja Stern, Vishvaksenan Rasiah, Veton Matoshi, et al.*2023 · 29 citations*](https://md.research.elamur.ai/free/en/papers/stern-2023-one-law-many-languages-benchmarking/) 15. [**Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters arXiv:2407.01406 · 2024.kallm-1.7**Daniil Gurgurov, Mareike Hartmann, Simon Ostermann*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/gurgurov-2024-adapting-multilingual-llms-low-resource/) 16. **[Bridging Argument Quality and Deliberative Quality Annotations with Adapters](https://md.research.elamur.ai/free/en/papers/falk-2023-bridging-argument-quality-deliberative-quality/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Neele Falk, Gabriella Lapesa*2023 · 21 citations* 17. **[Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks](https://md.research.elamur.ai/free/en/papers/li-2025-can-we-further-elicit-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xingxuan Li, Weiwen Xu, Ruochen Zhao, et al.*2025 · 13 citations* 18. [**Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models arXiv:2505.03469**Bin Yu, Hang Yuan, Haotian Li, et al.*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-long-short-chain-thought-mixture/) 19. **[LegalChainReasoner: Grounding Criminal Judicial Opinion Generation via Structured Legal Chains](https://md.research.elamur.ai/free/en/papers/shi-2026-legalchainreasoner-grounding-criminal-judicial-opinion/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weizhe Shi, Qiqi Wang, Yihong Pan, et al.*2026* 20. **[MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards](https://md.research.elamur.ai/free/en/papers/shen-2026-membuilder-reinforcing-llms-long-term/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyu Shen, Ziming Wu, Fuming Lai, et al.*2026* 171 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/hai-ye/) — Hai Ye