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Generation of plausible but incorrect factual information, often termed hallucination, has attracted significant research interest.
A mathematical theory of communication
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Dense passage retrieval for open-domain question answering
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Dense passage retrieval for open-domain question answering
Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and Yih, W.-t · 2004
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Leveraging passage retrieval with generative models for open domain question answering
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Leveraging passage retrieval with generative models for open domain question answering
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Reading wikipedia to answer open-domain questions
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
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Race: Large-scale reading comprehension dataset from examinations
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A survey on model compression for large language models
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Natural questions: a benchmark for question answering research
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Roberta: A robustly optimized bert pretraining approach, 2019
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Language models are few-shot learners
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Retrieval augmented language model pre-training
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Transformers are rnns: Fast autoregressive transformers with linear attention
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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On faithfulness and factuality in abstractive summarization
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Chatgpt: A large-scale generative model for conversation, 2020
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Recipes for building an open-domain chatbot
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On exposure bias, hallucination and domain shift in neural machine translation
Wang, C. and Sennrich, R · 2020
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Zaheer, M., Guruganesh, G., Dubey, K. A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., et al · 2020
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Reason first, then respond: Modular generation for knowledge-infused dialogue
Adolphs, L., Shuster, K., Urbanek, J., Szlam, A., and Weston, J · 2021
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Unitedqa: A hybrid approach for open domain question answering
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Hanna, M., Liu, O., and Variengien, A · 2023
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Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., and Pfister, T · 2023
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Chain of explanation: New prompting method to generate quality natural language explanation for implicit hate speech
Huang, F., Kwak, H., and An, J · 2023
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Sure: Improving open-domain question answering of llms via summarized retrieval
Kim, J., Nam, J., Mo, S., Park, J., Lee, S.-W., Seo, M., Ha, J.-W., and Shin, J · 2023
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Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y · 2023
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Cheng, H., Shen, Y., Liu, X., He, P., Chen, W., and Gao, J · 2021
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Automatic text summarization: A comprehensive survey
El-Kassas, W. S., Salama, C. R., Rafea, A. A., and Mohamed, H. K · 2021
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Scaling language models: Methods, analysis & insights from training gopher
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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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End-to-end training of multi-document reader and retriever for open-domain question answering
Singh, D., Reddy, S., Hamilton, W., Dyer, C., and Yogatama, D · 2021
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Retrieving and reading: A comprehensive survey on open-domain question answering
Zhu, F., Lei, W., Wang, C., Zheng, J., Poria, S., and Chua, T.-S · 2021
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Distribution-based measures of surprise for creative language: Experiments with humor and metaphor
Bunescu, R. and Uduehi, O. O · 2022
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Palm: Scaling language modeling with pathways, 2022
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
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Compressing context to enhance inference efficiency of large language models
Li, Y., Dong, B., Lin, C., and Guerin, F · 2023
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Deductive verification of chain-of-thought reasoning
Ling, Z., Fang, Y., Li, X., Huang, Z., Lee, M., Memisevic, R., and Su, H · 2023
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Lost in the middle: How language models use long contexts
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P · 2023
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Chain-of-skills: A configurable model for open-domain question answering
Ma, K., Cheng, H., Zhang, Y., Liu, X., Nyberg, E., and Gao, J · 2023
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Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al · 2023
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Astrollama: Towards specialized foundation models in astronomy
Nguyen, T. D., Ting, Y.-S., Ciucă, I., O’Neill, C., Sun, Z.-C., Jabłońska, M., Kruk, S., Perkowski, E., Miller, J., Li, J., et al · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., Chen, W., et al · 2023
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Factually consistent summarization via reinforcement learning with textual entailment feedback
Roit, P., Ferret, J., Shani, L., Aharoni, R., Cideron, G., Dadashi, R., Geist, M., Girgin, S., Hussenot, L., Keller, O., et al · 2023
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Questions are all you need to train a dense passage retriever
Sachan, D. S., Lewis, M., Yogatama, D., Zettlemoyer, L., Pineau, J., and Zaheer, M · 2023
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Large language models can be easily distracted by irrelevant context
Shi, F., Chen, X., Misra, K., Scales, N., Dohan, D., Chi, E. H., Schärli, N., and Zhou, D · 2023
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Contrastive learning reduces hallucination in conversations
Sun, W., Shi, Z., Gao, S., Ren, P., de Rijke, M., and Ren, Z · 2023
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Gemini: A family of highly capable multimodal models, 2023
Team, G · 2023
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Llama: Open and efficient foundation language models, 2023
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G · 2023
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Varshney, N., Yao, W., Zhang, H., Chen, J., and Yu, D · 2023
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Freshllms: Refreshing large language models with search engine augmentation
Vu, T., Iyyer, M., Wang, X., Constant, N., Wei, J., Wei, J., Tar, C., Sung, Y.-H., Zhou, D., Le, Q., et al · 2023
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Recomp: Improving retrieval-augmented lms with context compression and selective augmentation
Xu, F., Shi, W., and Choi, E · 2023
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Making retrieval-augmented language models robust to irrelevant context
Yoran, O., Wolfson, T., Ram, O., and Berant, J · 2023
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Atom: Low-bit quantization for efficient and accurate llm serving
Zhao, Y., Lin, C.-Y., Zhu, K., Ye, Z., Chen, L., Zheng, S., Ceze, L., Krishnamurthy, A., Chen, T., and Kasikci, B · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2023
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Deepseek llm: Scaling open-source language models with longtermism
Bi, X., Chen, D., Chen, G., Chen, S., Dai, D., Deng, C., Ding, H., Dong, K., Du, Q., Fu, Z., et al · 2024
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