--- title: "Who cites Evaluating ChatGPT's Information — 83 citing papers" description: "83 papers of the Elamur Research library cite «Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance…" canonical: "https://research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Evaluating ChatGPT's Information Extraction…](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/) / cited by # Who cites «Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness» 83 papers of our library cite it (of the 149,439 whose bibliographies we read) · 15 with a page here, the most cited first · [← back to «Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness»](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/) 1. **[Instruction Tuning for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 520 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. **[OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/liu-2023-ocrbench-hidden-mystery-ocr-large/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Yuliang Liu, Zhang Li, Mingxin Huang, et al.*2023 · 306 citations* 4. **[NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Oscar Sainz, Jon Campos, Iker García-Ferrero, et al.*2023 · 250 citations* 5. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations* 6. **[Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/zhang-2024-extract-define-canonicalize-llm-based/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bowen Zhang, Harold Soh*2024 · 155 citations* 7. [**UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition arXiv:2308.03279**Wenxuan Zhou, Sheng Zhang, Yu Gu, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-universalner-targeted-distillation-large-language/) 8. **[AutoRE: Document-Level Relation Extraction with Large Language Models](https://md.research.elamur.ai/free/en/papers/xue-2024-autore-document-level-relation-extraction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lilong Xue, Dan Zhang, Yuxiao Dong, et al.*2024 · 79 citations* 9. [**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/) 10. [**Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction arXiv:2306.14122**Feng Chen, Yujian Feng*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-chain-thought-prompt-distillation-multimodal/) 11. **[Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models](https://md.research.elamur.ai/free/en/papers/min-2024-synergetic-event-understanding-collaborative-approach/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingkai Min, Qipeng Guo, Xiangkun Hu, et al.*2024 · 19 citations* 12. **[Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/yang-2025-beyond-boundaries-learning-universal-entity/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yuming Yang, Wantong Zhao, Caishuang Huang, et al.*2025 · 11 citations* 13. **[MAVEN-FACT: A Large-scale Event Factuality Detection Dataset](https://md.research.elamur.ai/free/en/papers/li-2024-maven-fact-large-scale-event/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunyang Li, Hao Peng, Xiaozhi Wang, et al.*2024 · 6 citations* 14. **[How well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction](https://md.research.elamur.ai/free/en/papers/chanthran-2023-how-well-chatgpt-understand-malaysian/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Mohan Raj Chanthran, Lay-Ki Soon, Huey Fang Ong, et al.*2023 · 2 citations* 15. **[Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations](https://md.research.elamur.ai/free/en/papers/mtumbuka-2026-contrastively-pre-trained-event-embeddings/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Frank Mtumbuka, Steven Schockaert*2026* 68 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/) — Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness