🔗 Who cites «When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset»

121 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · ← back to «When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset»

1. On the Opportunities and Risks of Foundation ModelsarXiv:2108.07258Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,160 citations
2. LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language ModelsarXiv:2308.11462Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li2023 · 389 citations
3. Hallucination-Free? Assessing the Reliability of Leading AI Legal Research ToolsarXiv:2405.20362Varun Magesh, Faiz Surani, Matthew Dahl, et al.2024 · 271 citations
4. LexGLUE: A Benchmark Dataset for Legal Language Understanding in EnglisharXiv:2110.00976Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras2021 · 258 citations
5. FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving PerformancearXiv:2305.05176Lingjiao Chen, Matei Zaharia, James Zou2023 · 241 citations
6. LexGLUE: A Benchmark Dataset for Legal Language Understanding in EnglishAnnual Meeting of the Association for Computational Linguistics (ACL)Ilias Chalkidis, Abhik Jana, Dirk Hartung, et al.2022 · 239 citations
7. ContractNLI: A Dataset for Document-level Natural Language Inference for ContractsFindings of the Association for Computational LinguisticsYuta Koreeda, Christopher Manning2021 · 177 citations
8. Lawyer LLaMA Technical ReportarXiv:2305.15062Quzhe Huang, Mingxu Tao, Chen Zhang, et al.2023 · 157 citations
9. Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal DatasetConference on Neural Information Processing Systems (NeurIPS)Peter Henderson, Mark S. Krass, Lucia Zheng, et al.2022 · 133 citations
10. Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedbackarXiv:2303.05453Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, et al.2023 · 57 citations
11. A Reasoning-Focused Legal Retrieval BenchmarkarXiv:2505.03970Lucia Zheng, Neel Guha, Javokhir Arifov, et al.2025 · 55 citations
12. Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs?Language Resources and Evaluation Conference (LREC)Wangyue Li, Liangzhi Li, Tong Xiang, et al.2024 · 47 citations
13. One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial SupportarXiv:2306.09237Ronja Stern, Vishvaksenan Rasiah, Veton Matoshi, et al.2023 · 29 citations
14. Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?Annual Meeting of the Association for Computational Linguistics (ACL)Jiayu Liu, Qing Zong, Weiqi Wang, et al.2025 · 8 citations
15. OpenExempt: A Diagnostic Benchmark for Legal Reasoning and a Framework for Creating Custom Benchmarks on DemandFindings of the Association for Computational LinguisticsSergio Servantez, Sarah B. Lawsky, Rajiv Jain, et al.2026 · 2 citations
16. Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?Annual Meeting of the Association for Computational Linguistics (ACL)Lorenzo Molfetta, Alessio Cocchieri, Luca Ragazzi, et al.2026 · 2 citations
17. CuriosAI at SemEval-2026 Task 4: A Comprehensive Study of Zero-Shot versus Fine-Tuned Approaches for Narrative SimilarityInternational Workshop on Semantic Evaluation (SemEval)Yuki Shibata, Hiroki Takushima, Fumika Beppu, et al.2026 · 1 citation
18. AYN: A Tiny Yet Competitive Indian Legal Language Model Pretrained from ScratchLanguage Resources and Evaluation Conference (LREC)Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya2026
19. Compressing Language Models for Specialized DomainsConference of the European Chapter of the Association for Computational Linguistics (EACL)Miles Williams, George Chrysostomou, Vitor Amancio Jeronymo, et al.2026
20. LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific TasksAnnual Meeting of the Association for Computational Linguistics (ACL)Yuxuan Lu, Bingsheng Yao, Shao Zhang, et al.2026
21. Mitigating Structural Knowledge Collapse in Domain-Specific LLMs via Morpheme-Aware KV-AggregationAnnual Meeting of the Association for Computational Linguistics (ACL)Yuxuan Si, Zheqi Lv, Chengxi Zang, et al.2026
22. Pub-LawBench: Public-Oriented Benchmarking for LegalAIAnnual Meeting of the Association for Computational Linguistics (ACL)Qiaoyu Zheng, Zehan Ma, Yijing Zhang, et al.2026
23. RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes DomainsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhijie Tan, Xu Chu, Guanyu Wang, et al.2026

98 more papers of our library cite it; they have no page here yet.

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/zheng-2021-pretraining-help-assessing-self-supervised/cited-by/