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Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan T. McDonald. 2020 · 1919
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Tydi QA: A benchmark for information-seeking question answering in typologically diverse languages
Jonathan H. Clark, Jennimaria Palomaki, Vitaly Nikolaev, Eunsol Choi, Dan Garrette, Michael Collins, and Tom Kwiatkowski. 2020 · 2020
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Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick S. H. Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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ColBERT: Efficient and effective passage search via contextualized late interaction over BERT
Omar Khattab and Matei Zaharia. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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How context affects language models’ factual predictions
Fabio Petroni, Patrick S. H. Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2020 · 2020
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
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Leveraging passage retrieval with generative models for open domain question answering
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Adversarial examples for evaluating math word problem solvers
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The curious case of hallucinations in neural machine translation
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Retrieval augmentation reduces hallucination in conversation
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Improving language models by retrieving from trillions of tokens
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Retrieval-augmented generative question answering for event argument extraction
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Large language models can be easily distracted by irrelevant context
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
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Chain-of-thought prompting elicits reasoning in large language models
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Tevatron: An efficient and flexible toolkit for neural retrieval
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Enabling large language models to generate text with citations
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MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages
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A survey on evaluation of large language models
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Chain-of-verification reduces hallucination in large language models
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