mlsanityData to 7 Oct 2026

Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents

arXiv 2405.02732 ↗First posted 4 May 2024cs.CL cs.IRcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Methods for relation extraction from text mostly focus on high precision, at the cost of limited recall. High recall is crucial, though, to populate long lists of object entities that stand in a specific relation with a given subject. Cues for relevant objects can be spread across many passages in long texts. This poses the challenge of extracting long lists from long texts. We present the L3X method which tackles the problem in two stages: (1) recall-oriented generation using a large language model (LLM) with judicious techniques for retrieval augmentation, and (2) precision-oriented scrutinization to validate or prune candidates. Our L3X method outperforms LLM-only generations by a substantial margin.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 39 of its 73 references that are in the map. First posted on arXiv 4 May 2024.

Builds on

39 of 73 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 38

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2023A Survey of Large Language Modelscited by 2,537
  4. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  5. 2019Language Models as Knowledge Bases?cited by 1,922
  6. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  7. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  8. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  9. 2019How Can We Know What Language Models Know?cited by 953
  10. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  11. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
  12. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
26 more
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  2. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  3. 2021Editing Factual Knowledge in Language Modelscited by 548
  4. 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 481
  5. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 452
  6. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 376
  7. 2021Factual Probing Is [MASK]: Learning vs. Learning to Recallcited by 291
  8. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  9. 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
  10. 2023Fine-tuning Language Models for Factualitycited by 219
  11. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  12. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
  13. 2021KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extractioncited by 180
  14. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 167
  15. 2023Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4cited by 123
  16. 2020Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queriescited by 107
  17. 2019E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTcited by 96
  18. 2021GenIE: Generative Information Extractioncited by 53
  19. 2020More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extractioncited by 52
  20. 2020Global-to-Local Neural Networks for Document-Level Relation Extractioncited by 43
  21. 2023Language Models Hallucinate, but May Excel at Fact Verificationcited by 35
  22. 2021Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusioncited by 28
  23. 2022Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Datacited by 27
  24. 2022Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibrationcited by 24
  25. 2023Evaluating the Knowledge Base Completion Potential of GPTcited by 9
  26. 2023Extracting Multi-valued Relations from Language Modelscited by 3

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Citing papers per year: 2024: 1.

1 paper.

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