---
title: "Who cites You Chen — 127 papers of our library"
description: "127 papers of the Elamur Research library cite the works of You Chen, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/you-chen/cited-by/"
---
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# Who cites You Chen

128 citations of their works from 127 papers of our library (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← You Chen](https://md.research.elamur.ai/free/en/authors/you-chen/)

1. **[Large language models are few-shot clinical information extractors](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.*2022 · 454 citations*
2. **[Large Language Models for Generative Information Extraction: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2023-large-language-models-generative-information/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Derong Xu, Wei Chen, Wenjun Peng, et al.*2023 · 406 citations*
3. **[Large Language Models are Few-Shot Clinical Information Extractors](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-few-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.*2022 · 384 citations*
4. **[GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://md.research.elamur.ai/free/en/papers/wan-2023-gpt-re-context-learning-relation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Wan, Fei Cheng, Zhuoyuan Mao, et al.*2023 · 253 citations*
5. **[Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yubo Ma, Yixin Cao, Yong Hong, et al.*2023 · 212 citations*
6. **[Sources of Hallucination by Large Language Models on Inference Tasks](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 212 citations*
7. [**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/)
8. [**Can GPT-3 Perform Statutory Reasoning? arXiv:2302.06100**Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/blair-stanek-2023-gpt-3-perform-statutory-reasoning/)
9. **[SciMON: Scientific Inspiration Machines Optimized for Novelty](https://md.research.elamur.ai/free/en/papers/wang-2023-scimon-scientific-inspiration-machines/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyun Wang, Doug Downey, Heng Ji, et al.*2023 · 131 citations*
10. **[Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors](https://md.research.elamur.ai/free/en/papers/zhang-2023-aligning-instruction-tasks-unlocks-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Zhang, Bernal Jimenez Gutierrez, Yu Su*2023 · 118 citations*
11. **[CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://md.research.elamur.ai/free/en/papers/li-2023-codeie-large-code-generation-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peng Li, Tianxiang Sun, Qiong Tang, et al.*2023 · 109 citations*
12. **[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*
13. [**Self-Verification Improves Few-Shot Clinical Information Extraction arXiv:2306.00024 · IMLH 2023**Zelalem Gero, Chandan Singh, Hao Cheng, et al.*2023 · 51 citations*](https://md.research.elamur.ai/free/en/papers/gero-2023-self-verification-improves-few-shot/)
14. [**When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks arXiv:2311.08993**Hao Peng, Xiaozhi Wang, Jianhui Chen, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-context-learning-fall-short-why/)
15. **[Overview of the MEDIQA-SYNUR 2026 Shared Task on Observation Extraction from Nurse Dictations](https://md.research.elamur.ai/free/en/papers/michalopoulos-2026-overview-mediqa-synur-2026-shared/) [Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC](https://md.research.elamur.ai/free/en/venues/clinicalnlp/)**George Michalopoulos, Jean-Philippe Corbeil, Cari Bader, et al.*2026 · 9 citations*
16. **[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*
17. **[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*
18. **[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*
19. **[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*
20. [**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/)
21. **[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*
22. **[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*
23. **[Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/zhang-2026-scaling-performance-low-resource-annotation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qi Zhang, Fangping Lan, Cornelia Caragea, et al.*2026*
24. **[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*
25. **[Where do LLMs currently stand on biomedical NER in both clean and noisy settings ?](https://md.research.elamur.ai/free/en/papers/ye-2026-where-do-llms-currently-stand/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Christophe Ye, Cassie S. Mitchell*2026*

102 more citing papers of our library have no page here yet.

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