mlsanityData to 7 Oct 2026

InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction

arXiv 2304.08085 ↗First posted 17 Apr 2023cs.CL cs.AIcited by 167

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Abstract (arXiv)

Large language models have unlocked strong multi-task capabilities from reading instructive prompts. However, recent studies have shown that existing large models still have difficulty with information extraction tasks. For example, gpt-3.5-turbo achieved an F1 score of 18.22 on the Ontonotes dataset, which is significantly lower than the state-of-the-art performance. In this paper, we propose InstructUIE, a unified information extraction framework based on instruction tuning, which can uniformly model various information extraction tasks and capture the inter-task dependency. To validate the proposed method, we introduce IE INSTRUCTIONS, a benchmark of 32 diverse information extraction datasets in a unified text-to-text format with expert-written instructions. Experimental results demonstrate that our method achieves comparable performance to Bert in supervised settings and significantly outperforms the state-of-the-art and gpt3.5 in zero-shot settings.

Key facts. Cited by 167 papers in the map (data through 7 Oct 2026). Builds on 39 of its 71 references that are in the map. First posted on arXiv 17 Apr 2023.

Builds on

39 of 71 references.

References · 39

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 858
  6. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
  7. 2021Cross-Task Generalization via Natural Language Crowdsourcing Instructionscited by 696
  8. 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 321
  9. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  10. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 244
  11. 2022Unified Structure Generation for Universal Information Extractioncited by 220
  12. 2019Dice Loss for Data-imbalanced NLP Taskscited by 180
27 more
  1. 2021Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extractioncited by 129
  2. 2020CrossNER: Evaluating Cross-Domain Named Entity Recognitioncited by 102
  3. 2021A Unified Generative Framework for Aspect-Based Sentiment Analysiscited by 97
  4. 2020Automated Concatenation of Embeddings for Structured Predictioncited by 86
  5. 2022RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extractioncited by 60
  6. 2023How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Taskscited by 58
  7. 2018A Hierarchical Framework for Relation Extraction with Reinforcement Learningcited by 49
  8. 2023Universal Information Extraction as Unified Semantic Matchingcited by 47
  9. 2015Relation Classification via Recurrent Neural Networkcited by 46
  10. 2021ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learningcited by 46
  11. 2020FLERT: Document-Level Features for Named Entity Recognitioncited by 45
  12. 2022DeepStruct: Pretraining of Language Models for Structure Predictioncited by 45
  13. 2021UniRE: A Unified Label Space for Entity Relation Extractioncited by 38
  14. 2020A Frustratingly Easy Approach for Entity and Relation Extractioncited by 37
  15. 2022T-NER: An All-Round Python Library for Transformer-based Named Entity Recognitioncited by 37
  16. 2022PHEE: A Dataset for Pharmacovigilance Event Extraction from Textcited by 36
  17. 2018Improving Distantly Supervised Relation Extraction using Word and Entity Based Attentioncited by 35
  18. 2014POLYGLOT-NER: Massive Multilingual Named Entity Recognitioncited by 32
  19. 2022InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERcited by 31
  20. 2020Biomedical Named Entity Recognition at Scalecited by 30
  21. 2022Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shiftscited by 27
  22. 2022Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learningcited by 24
  23. 2019GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labelingcited by 22
  24. 2022MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspectivecited by 17
  25. 2021Revisiting the Negative Data of Distantly Supervised Relation Extractioncited by 15
  26. 2022Zero-shot Triplet Extraction by Template Infillingcited by 7
  27. 2023An Information Extraction Study: Take In Mind the Tokenization!cited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 37; 2024: 62; 2025: 39; 2026: 29.

167 papers.

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  1. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  2. 2023Orthogonal Subspace Learning for Language Model Continual Learningcited by 296
  3. 2023FireAct: Toward Language Agent Fine-tuningcited by 247
  4. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
  5. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  6. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 116
  7. 2024Large Language Models Meet NLP: A Surveycited by 102
  8. 2024Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Modelscited by 97
  9. 2023GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformercited by 84
  10. 2023Large Language Models and Knowledge Graphs: Opportunities and Challengescited by 71
  11. 2023UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognitioncited by 70
  12. 2024InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignmentcited by 56
38 more
  1. 2023An Empirical Study on Information Extraction using Large Language Modelscited by 46
  2. 2023PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMscited by 44
  3. 2023ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Modelscited by 44
  4. 2023Retrieval-Augmented Code Generation for Universal Information Extractioncited by 40
  5. 2024SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literaturecited by 37
  6. 2024Unveiling the Generalization Power of Fine-Tuned Large Language Modelscited by 35
  7. 2024NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Datacited by 34
  8. 2023Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistancecited by 33
  9. 2024KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extractioncited by 33
  10. 2024Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extractioncited by 33
  11. 2023Revisiting Large Language Models as Zero-shot Relation Extractorscited by 29
  12. 2023TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extractioncited by 27
  13. 2024Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labelingcited by 24
  14. 2024ADELIE: Aligning Large Language Models on Information Extractioncited by 24
  15. 2023GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuningcited by 23
  16. 2024C-ICL: Contrastive In-context Learning for Information Extractioncited by 22
  17. 2023Label Supervised LLaMA Finetuningcited by 20
  18. 2023YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extractioncited by 19
  19. 2023SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understandingcited by 18
  20. 2023Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Explorationcited by 18
  21. 2025Instruction-Tuning LLMs for Event Extraction with Annotation Guidelinescited by 18
  22. 2025SLOT: Structuring the Output of Large Language Modelscited by 18
  23. 2023Specialist or Generalist? Instruction Tuning for Specific NLP Taskscited by 17
  24. 2024A Simple but Effective Approach to Improve Structured Language Model Output for Information Extractioncited by 16
  25. 2024AutoRE: Document-Level Relation Extraction with Large Language Modelscited by 16
  26. 2024Rethinking Negative Instances for Generative Named Entity Recognitioncited by 13
  27. 2024Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractorscited by 13
  28. 2024BEYOND DIALOGUE: A Profile-Dialogue Alignment Framework Towards General Role-Playing Language Modelcited by 13
  29. 2023Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Modelcited by 12
  30. 2023Mirror: A Universal Framework for Various Information Extraction Taskscited by 12
  31. 2024LLMs in Biomedicine: A study on clinical Named Entity Recognitioncited by 12
  32. 2024LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognitioncited by 11
  33. 2023PIVOINE: Instruction Tuning for Open-world Information Extractioncited by 10
  34. 2024Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extractioncited by 10
  35. 2023DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognitioncited by 9
  36. 2023InstructIE: A Bilingual Instruction-based Information Extraction Datasetcited by 9
  37. 2024ToNER: Type-oriented Named Entity Recognition with Generative Language Modelcited by 8
  38. 2024A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classificationcited by 8

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