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The Retrieval-Augmented Language Model (RALM) has shown remarkable performance on knowledge-intensive tasks by incorporating external knowledge during inference, which mitigates the factual hallucinations inherited in large language models (LLMs).
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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How context affects language models’ factual predictions
Petroni, F.; Lewis, P.; Piktus, A.; Rocktäschel, T.; Wu, Y.; Miller, A. H.; and Riedel, S. 2020 · 2005
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FEVER: a Large-scale Dataset for Fact Extraction and VERification
Thorne, J.; Vlachos, A.; Christodoulopoulos, C.; and Mittal, A. 2018 · 2018
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Natural Questions: A Benchmark for Question Answering Research
Kwiatkowski, T.; Palomaki, J.; Redfield, O.; Collins, M.; Parikh, A.; Alberti, C.; Epstein, D.; Polosukhin, I.; Devlin, J.; Lee, K.; Toutanova, K.; Jones, L.; Kelcey, M.; Chang, M.-W.; Dai, A. M.; Uszkoreit, J.; Le, Q.; and Petrov, S. 2019 · 2019
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REALM: retrieval-augmented language model pre-training
Guu, K.; Lee, K.; Tung, Z.; Pasupat, P.; and Chang, M.-W. 2020 · 2020
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Dense Passage Retrieval for Open-Domain Question Answering
Karpukhin, V.; Oguz, B.; Min, S.; Lewis, P.; Wu, L.; Edunov, S.; Chen, D.; and Yih, W.-t. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.-t.; Rocktäschel, T.; et al. 2020 · 2020
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AmbigQA: Answering Ambiguous Open-domain Questions
Min, S.; Michael, J.; Hajishirzi, H.; and Zettlemoyer, L. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J.; Rajbhandari, S.; Ruwase, O.; and He, Y. 2020 · 2020
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Unsupervised dense information retrieval with contrastive learning
Izacard, G.; Caron, M.; Hosseini, L.; Riedel, S.; Bojanowski, P.; Joulin, A.; and Grave, E. 2021 · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Nakano, R.; Hilton, J.; Balaji, S.; Wu, J.; Ouyang, L.; Kim, C.; Hesse, C.; Jain, S.; Kosaraju, V.; Saunders, W.; et al. 2021 · 2021
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KILT: a Benchmark for Knowledge Intensive Language Tasks
Petroni, F.; Piktus, A.; Fan, A.; Lewis, P.; Yazdani, M.; De Cao, N.; Thorne, J.; Jernite, Y.; Karpukhin, V.; Maillard, J.; Plachouras, V.; Rocktäschel, T.; and Riedel, S. 2021 · 2021
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Improving language models by retrieving from trillions of tokens
Borgeaud, S.; Mensch, A.; Hoffmann, J.; Cai, T.; Rutherford, E.; Millican, K.; Van Den Driessche, G. B.; Lespiau, J.-B.; Damoc, B.; Clark, A.; et al. 2022 · 2022
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TRUE: Re-evaluating factual consistency evaluation
Honovich, O.; Aharoni, R.; Herzig, J.; Taitelbaum, H.; Kukliansy, D.; Cohen, V.; Scialom, T.; Szpektor, I.; Hassidim, A.; and Matias, Y. 2022 · 2022
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Few-shot learning with retrieval augmented language models
Izacard, G.; Lewis, P.; Lomeli, M.; Hosseini, L.; Petroni, F.; Schick, T.; Dwivedi-Yu, J.; Joulin, A.; Riedel, S.; and Grave, E. 2022 · 2022
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Teaching language models to support answers with verified quotes
Menick, J.; Trebacz, M.; Mikulik, V.; Aslanides, J.; Song, F.; Chadwick, M.; Glaese, M.; Young, S.; Campbell-Gillingham, L.; Irving, G.; et al. 2022 · 2022
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Large Dual Encoders Are Generalizable Retrievers
Ni, J.; Qu, C.; Lu, J.; Dai, Z.; Hernandez Abrego, G.; Ma, J.; Zhao, V.; Luan, Y.; Hall, K.; Chang, M.-W.; and Yang, Y. 2022 · 2022
Cited alongside, same era.
ASQA: Factoid Questions Meet Long-Form Answers
Stelmakh, I.; Luan, Y.; Dhingra, B.; and Chang, M.-W. 2022 · 2022
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Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang, X.; Wei, J.; Schuurmans, D.; Le, Q.; hsin Chi, E. H.; and Zhou, D. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
Gpt-4 technical report. arxiv 2303.08774
OpenAI, R. 2023 · 2023
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Measuring and Narrowing the Compositionality Gap in Language Models
Press, O.; Zhang, M.; Min, S.; Schmidt, L.; Smith, N.; and Lewis, M. 2023 · 2023
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In-Context Retrieval-Augmented Language Models
Ram, O.; Levine, Y.; Dalmedigos, I.; Muhlgay, D.; Shashua, A.; Leyton-Brown, K.; and Shoham, Y. 2023 · 2023
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Toolformer: Language models can teach themselves to use tools
Schick, T.; Dwivedi-Yu, J.; Dessì, R.; Raileanu, R.; Lomeli, M.; Zettlemoyer, L.; Cancedda, N.; and Scialom, T. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023 · 2023
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Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Zhou, D.; Scharli, N.; Hou, L.; Wei, J.; Scales, N.; Wang, X.; Schuurmans, D.; Bousquet, O.; Le, Q.; and hsin Chi, E. H. 2022 · 2022
Cited alongside, same era.
Knowledge-Augmented Language Model Verification
Baek, J.; Jeong, S.; Kang, M.; Park, J.; and Hwang, S. 2023 · 2023
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Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality
Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; Stoica, I.; and Xing, E. P. 2023 · 2023
Cited alongside, same era.
Enabling Large Language Models to Generate Text with Citations
Gao, T.; Yen, H.; Yu, J.; and Chen, D. 2023b · 2023
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Survey of hallucination in natural language generation
Ji, Z.; Lee, N.; Frieske, R.; Yu, T.; Su, D.; Xu, Y.; Ishii, E.; Bang, Y. J.; Madotto, A.; and Fung, P. 2023 · 2023
Cited alongside, same era.
Active Retrieval Augmented Generation
Jiang, Z.; Xu, F.; Gao, L.; Sun, Z.; Liu, Q.; Dwivedi-Yu, J.; Yang, Y.; Callan, J.; and Neubig, G. 2023 · 2023
Cited alongside, same era.
Efficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W.; Li, Z.; Zhuang, S.; Sheng, Y.; Zheng, L.; Yu, C. H.; Gonzalez, J. E.; Zhang, H.; and Stoica, I. 2023 · 2023
Cited alongside, same era.
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
Trivedi, H.; Balasubramanian, N.; Khot, T.; and Sabharwal, A. 2023 · 2023
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Xu, S.; Pang, L.; Shen, H.; Cheng, X.; and Chua, T.-s. 2023 · 2023
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ReAct: Synergizing Reasoning and Acting in Language Models
Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K.; and Cao, Y. 2023 · 2023
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Making retrieval-augmented language models robust to irrelevant context
Yoran, O.; Wolfson, T.; Ram, O.; and Berant, J. 2023 · 2023
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Chain-of-note: Enhancing robustness in retrieval-augmented language models
Yu, W.; Zhang, H.; Pan, X.; Ma, K.; Wang, H.; and Yu, D. 2023 · 2023
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Lima: Less is more for alignment
Zhou, C.; Liu, P.; Xu, P.; Iyer, S.; Sun, J.; Mao, Y.; Ma, X.; Efrat, A.; Yu, P.; Yu, L.; et al. 2023 · 2023
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Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Asai, A.; Wu, Z.; Wang, Y.; Sil, A.; and Hajishirzi, H. 2024 · 2024
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Chain-of-action: Faithful and multimodal question answering through large language models
Pan, Z.; Luo, H.; Li, M.; and Liu, H. 2024 · 2024
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RECOMP: Improving Retrieval-Augmented LMs with Context Compression and Selective Augmentation
Xu, F.; et al. 2024 · 2024
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Corrective retrieval augmented generation
Yan, S.-Q.; Gu, J.-C.; Zhu, Y.; and Ling, Z.-H. 2024 · 2024
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