mlsanityData to 7 Oct 2026

Zero-Shot Relation Extraction via Reading Comprehension

arXiv 1706.04115 ↗First posted 13 Jun 2017cs.CL cs.AI cs.LGcited by 425

Not on the map. Why?

Cited by 24 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.

Key facts. Cited by 425 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 58; 2022: 55; 2023: 62; 2024: 98; 2025: 87; 2026: 65.

425 papers.

Sort:

By area:

  1. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  2. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  3. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  5. 2022Mass-Editing Memory in a Transformercited by 976
  6. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  7. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  8. 2021Editing Factual Knowledge in Language Modelscited by 548
  9. 2021Fast Model Editing at Scalecited by 491
  10. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  11. 2021MetaICL: Learning to Learn In Contextcited by 450
  12. 2022Memory-Based Model Editing at Scalecited by 443
38 more
  1. 2023Editing Large Language Models: Problems, Methods, and Opportunitiescited by 367
  2. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 329
  3. 2022PromptSource: An Integrated Development Environment and Repository for Natural Language Promptscited by 288
  4. 2023Can We Edit Factual Knowledge by In-Context Learning?cited by 284
  5. 2022Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptorscited by 267
  6. 2022Prompting GPT-3 To Be Reliablecited by 258
  7. 2023Evaluating the Ripple Effects of Knowledge Editing in Language Modelscited by 238
  8. 2023Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Modelscited by 237
  9. 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 228
  10. 2024Generative Representational Instruction Tuningcited by 227
  11. 2024AlphaEdit: Null-Space Constrained Knowledge Editing for Language Modelscited by 224
  12. 2022Unified Structure Generation for Universal Information Extractioncited by 220
  13. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  14. 2023Transformer-Patcher: One Mistake worth One Neuroncited by 196
  15. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuningcited by 194
  16. 2023Knowledge Editing for Large Language Models: A Surveycited by 189
  17. 2021Towards Continual Knowledge Learning of Language Modelscited by 172
  18. 2023PMET: Precise Model Editing in a Transformercited by 170
  19. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 165
  20. 2024A Comprehensive Study of Knowledge Editing for Large Language Modelscited by 157
  21. 2022A Review on Language Models as Knowledge Basescited by 156
  22. 2023Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attackscited by 156
  23. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  24. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 116
  25. 2022Autoregressive Search Engines: Generating Substrings as Document Identifierscited by 112
  26. 2022Re2G: Retrieve, Rerank, Generatecited by 102
  27. 2024Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescuecited by 101
  28. 2024The Art of Saying No: Contextual Noncompliance in Language Modelscited by 100
  29. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  30. 2023EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Modelscited by 97
  31. 2024Model Editing at Scale leads to Gradual and Catastrophic Forgettingcited by 92
  32. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  33. 2024MEMORYLLM: Towards Self-Updatable Large Language Modelscited by 89
  34. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  35. 2024Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communitiescited by 84
  36. 2023MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRAcited by 80
  37. 2021Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefscited by 79
  38. 2021SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoningcited by 75

50 most cited of 425 All 425 (JSON)