Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

647 papers of our library cite it (of the 140673 whose bibliographies we read); 42 of them have a page here, 605 not yet.

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• LongT5: Efficient Text-To-Text Transformer for Long Sequences
• Unsupervised Dense Information Retrieval with Contrastive Learning
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
• LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
• Dense Passage Retrieval for Open-Domain Question Answering
• RealTime QA: What's the Answer Right Now?
• Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
• Internet-Augmented Dialogue Generation
• Hurdles to Progress in Long-form Question Answering
• Internet-augmented language models through few-shot prompting for open-domain question answering
• PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
• RA-DIT: Retrieval-Augmented Dual Instruction Tuning
• Lost in the Middle: How Language Models Use Long Contexts
• Entity-Based Knowledge Conflicts in Question Answering
• Generation-Augmented Retrieval for Open-domain Question Answering
• Teaching language models to support answers with verified quotes
• Augmented Language Models: a Survey
• PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations
• RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering
• Improving Passage Retrieval with Zero-Shot Question Generation
• LaMP: When Large Language Models Meet Personalization
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
• REPLUG: Retrieval-Augmented Black-Box Language Models
• Retrieval Augmentation Reduces Hallucination in Conversation
• BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
• ASQA: Factoid Questions Meet Long-Form Answers
• One Embedder, Any Task: Instruction-Finetuned Text Embeddings
• LaMDA: Language Models for Dialog Applications
• Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
• Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts
• Retrieval meets Long Context Large Language Models
• LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
• Generate rather than Retrieve: Large Language Models are Strong Context Generators
• CREPE: Open-Domain Question Answering with False Presuppositions
• IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions
• Big Bird: Transformers for Longer Sequences
• A Survey of Large Language Models

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.eacl-main.74 · arXiv 2007.01282

@inproceedings{izacard2021leveraging,
  title = {{Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering}},
  author = {Izacard, Gautier and Grave, Edouard},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  year = {2021},
  doi = {10.18653/v1/2021.eacl-main.74},
  eprint = {2007.01282},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.eacl-main.74}
}

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