mlsanityData to 14 Oct 2026

Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness

arXiv 2304.11633 ↗First posted 23 Apr 2023cs.CLcited by 130

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Abstract (arXiv)

The capability of Large Language Models (LLMs) like ChatGPT to comprehend user intent and provide reasonable responses has made them extremely popular lately. In this paper, we focus on assessing the overall ability of ChatGPT using 7 fine-grained information extraction (IE) tasks. Specially, we present the systematically analysis by measuring ChatGPT's performance, explainability, calibration, and faithfulness, and resulting in 15 keys from either the ChatGPT or domain experts. Our findings reveal that ChatGPT's performance in Standard-IE setting is poor, but it surprisingly exhibits excellent performance in the OpenIE setting, as evidenced by human evaluation. In addition, our research indicates that ChatGPT provides high-quality and trustworthy explanations for its decisions. However, there is an issue of ChatGPT being overconfident in its predictions, which resulting in low calibration. Furthermore, ChatGPT demonstrates a high level of faithfulness to the original text in the majority of cases. We manually annotate and release the test sets of 7 fine-grained IE tasks contains 14 datasets to further promote the research. The datasets and code are available at https://github.com/pkuserc/ChatGPT_for_IE.

Key facts. Cited by 130 papers in the map (data through 14 Oct 2026). Builds on 71 of its 106 references that are in the map. First posted on arXiv 23 Apr 2023.

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71 of 106 references.

References · 71

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Citing papers per year: 2023: 52; 2024: 44; 2025: 27; 2026: 7.

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  1. 2023Instruction Tuning for Large Language Models: A Surveycited by 623
  2. 2023OCRBench: On the Hidden Mystery of OCR in Large Multimodal Modelscited by 476
  3. 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmarkcited by 348
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  6. 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
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  8. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunitiescited by 97
  9. 2023UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognitioncited by 72
  10. 2023Large Language Models and Knowledge Graphs: Opportunities and Challengescited by 71
  11. 2023Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofittingcited by 70
  12. 2024Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Constructioncited by 70
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  3. 2023An Empirical Study on Information Extraction using Large Language Modelscited by 46
  4. 2023Retrieval-Augmented Code Generation for Universal Information Extractioncited by 40
  5. 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
  6. 2023Instruct and Extract: Instruction Tuning for On-Demand Information Extractioncited by 36
  7. 2023Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysiscited by 35
  8. 2023TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extractioncited by 28
  9. 2025One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systemscited by 28
  10. 2023Primacy Effect of ChatGPTcited by 27
  11. 2023Iterative Zero-Shot LLM Prompting for Knowledge Graph Constructioncited by 25
  12. 2023DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articlescited by 24
  13. 2024ADELIE: Aligning Large Language Models on Information Extractioncited by 24
  14. 2024C-ICL: Contrastive In-context Learning for Information Extractioncited by 23
  15. 2023Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuningcited by 21
  16. 2023Label Supervised LLaMA Finetuningcited by 20
  17. 2025Instruction-Tuning LLMs for Event Extraction with Annotation Guidelinescited by 18
  18. 2024AutoRE: Document-Level Relation Extraction with Large Language Modelscited by 16
  19. 2023Self-Improving for Zero-Shot Named Entity Recognition with Large Language Modelscited by 15
  20. 2023Enhancing the Rationale-Input Alignment for Self-explaining Rationalizationcited by 15
  21. 2023Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Headscited by 14
  22. 2023Rationale-Enhanced Language Models are Better Continual Relation Learnerscited by 13
  23. 2024Rethinking Negative Instances for Generative Named Entity Recognitioncited by 13
  24. 2023An Assessment of ChatGPT on Log Datacited by 12
  25. 2024ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinementcited by 12
  26. 2024The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMscited by 12
  27. 2024Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answeringcited by 12
  28. 2023Language Models as a Service: Overview of a New Paradigm and its Challengescited by 10
  29. 2024Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extractioncited by 10
  30. 2023InstructIE: A Bilingual Instruction-based Information Extraction Datasetcited by 9
  31. 2023Contextual Label Projection for Cross-Lingual Structured Predictioncited by 9
  32. 2023Distilling Rule-based Knowledge into Large Language Modelscited by 9
  33. 2024On-the-fly Definition Augmentation of LLMs for Biomedical NERcited by 9
  34. 2024Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Redditcited by 9
  35. 2024RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command Explainercited by 9
  36. 2024Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generationcited by 8
  37. 2024OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction Systemcited by 8
  38. 2025Towards Better Evaluation for Generated Patent Claimscited by 8

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