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We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation.
Reading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., and Bordes, A · 2017
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Can a suit of armor conduct electricity? A new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., and Manning, C. D · 2018
Earlier work this paper cites.
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
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Retrieval augmented language model pre-training
Guu, K., Lee, K., Tung, Z., Pasupat, P., and Chang, M · 2020
Earlier work this paper cites.
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., Riedel, S., and Kiela, D · 2020
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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
Earlier work this paper cites.
Natural language processing advancements by deep learning: A survey
Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N., and Fox, E. A · 2020
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Did Aristotle use a laptop? A question answering benchmark with implicit reasoning strategies
Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., and Berant, J · 2021
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Retrieval augmentation reduces hallucination in conversation
Shuster, K., Poff, S., Chen, M., Kiela, D., and Weston, J · 2021
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Adaptive semiparametric language models
Yogatama, D., de Masson d’Autume, C., and Kong, L · 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., De Las Casas, D., Guy, A., Menick, J., Ring, R., Hennigan, T., Huang, S., Maggiore, L., Jones, C., Cassirer, A., Brock, A., Paganini, M., Irving, G., Vinyals, O., Osindero, S., Simonyan, K., Rae, J., Elsen, E., and Sifre, L · 2022
Earlier work this paper cites.
You can’t pick your neighbors, or can you? When and how to rely on retrieval in the kNN-LM
Drozdov, A., Wang, S., Rahimi, R., Mccallum, A., Zamani, H., and Iyyer, M · 2022
Earlier work this paper cites.
Unsupervised dense information retrieval with contrastive learning
Izacard, G., Caron, M., Hosseini, L., Riedel, S., Bojanowski, P., Joulin, A., and Grave, E · 2022
Earlier work this paper cites.
Mallen, A., Asai, A., Zhong, V., Das, R., Hajishirzi, H., and Khashabi, D · 2022
Cited alongside, same era.
Is a question decomposition unit all we need?
Patel, P., Mishra, S., Parmar, M., and Baral, C · 2022
Cited alongside, same era.
Musique: Multihop questions via single-hop question composition
Trivedi, H., Balasubramanian, N., Khot, T., and Sabharwal, A · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Self-RAG: Learning to retrieve, generate, and critique through self-reflection
Asai, A., Wu, Z., Wang, Y., Sil, A., and Hajishirzi, H · 2023
Cited alongside, same era.
A survey of large language models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al · 2023
Later among the works it cites.
Least-to-most prompting enables complex reasoning in large language models
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q. V., and Chi, E. H · 2023
Later among the works it cites.
RQ-RAG: Learning to refine queries for retrieval augmented generation
Chan, C.-M., Xu, C., Yuan, R., Luo, H., Xue, W., Guo, Y., and Fu, J · 2024
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When is tree search useful for llm planning? it depends on the discriminator
Chen, Z., White, M., Mooney, R., Payani, A., Su, Y., and Sun, H · 2024
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Grounding by trying: LLMs with reinforcement learning-enhanced retrieval
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Reasoning with language model is planning with world model
Hao, S., Gu, Y., Ma, H., Hong, J. J., Wang, Z., Wang, D. Z., and Hu, Z · 2023
Cited alongside, same era.
Atlas: 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 · 2023
Cited alongside, same era.
Active retrieval augmented generation
Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G · 2023
Cited alongside, same era.
Search augmented instruction learning
Luo, H., Zhang, T., Chuang, Y.-S., Gong, Y., Kim, Y., Wu, X., Meng, H. M., and Glass, J. R · 2023
Cited alongside, same era.
Query rewriting in retrieval-augmented large language models
Ma, X., Gong, Y., He, P., Zhao, H., and Duan, N · 2023
Cited alongside, same era.
Med-halt: Medical domain hallucination test for large language models
Pal, A., Umapathi, L. K., and Sankarasubbu, M · 2023
Cited alongside, same era.
In-context retrieval-augmented language models
Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., and Shoham, Y · 2023
Cited alongside, same era.
Hsu, S., Khattab, O., Finn, C., and Sharma, A · 2024
Closest in time.
Long context rag performance of large language models
Leng, Q., Portes, J., Havens, S., Zaharia, M., and Carbin, M · 2024
Closest in time.
Ra-isf: Learning to answer and understand from retrieval augmentation via iterative self-feedback
Liu, Y., Peng, X., Zhang, X., Liu, W., Yin, J., Cao, J., and Du, T · 2024
Closest in time.
Eliciting critical reasoning in retrieval-augmented language models via contrastive explanations
Ranaldi, L., Valentino, M., and Freitas, A · 2024
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Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2024
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Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph
Sun, J., Xu, C., Tang, L., Wang, S., Lin, C., Gong, Y., Ni, L., Shum, H.-Y., and Guo, J · 2024
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Multihop-RAG: Benchmarking retrieval-augmented generation for multi-hop queries
Tang, Y. and Yang, Y · 2024
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From decoding to meta-generation: Inference-time algorithms for large language models
Welleck, S., Bertsch, A., Finlayson, M., Schoelkopf, H., Xie, A., Neubig, G., Kulikov, I., and Harchaoui, Z · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
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Revolutionizing finance with llms: An overview of applications and insights
Zhao, H., Liu, Z., Wu, Z., Li, Y., Yang, T., Shu, P., Xu, S., Dai, H., Zhao, L., Mai, G., et al · 2024
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