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We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated responses.
Nltk: the natural language toolkit
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Rationalizing neural predictions
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Dureader: a chinese machine reading comprehension dataset from real-world applications
He, W., Liu, K., Liu, J., Lyu, Y., Zhao, S., Xiao, X., Liu, Y., Wang, Y., Wu, H., She, Q., et al · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
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The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2020
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Efficient attentions for long document summarization
Huang, L., Cao, S., Parulian, N., Ji, H., and Wang, L · 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
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al · 2022
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Rarr: Researching and revising what language models say, using language models
Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., Fan, Y., Zhao, V. Y., Lao, N., Lee, H., Juan, D.-C., et al · 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
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Introducing chatgpt, November 2022
OpenAI · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., L. Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
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Longbench: A bilingual, multitask benchmark for long context understanding
Bai, Y., Lv, X., Zhang, J., Lyu, H., Tang, J., Huang, Z., Du, Z., Liu, X., Zeng, A., Hou, L., et al · 2023
Cited alongside, same era.
Enabling large language models to generate text with citations
Gao, T., Yen, H., Yu, J., and Chen, D · 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
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Aligning large language models through synthetic feedback
Kim, S., Bae, S., Shin, J., Kang, S., Kwak, D., Yoo, K., and Seo, M · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Unifying corroborative and contributive attributions in large language models
Advancing large language model attribution through self-improving
Huang, L., Feng, X., Ma, W., Zhao, L., Fan, Y., Zhong, W., Xu, D., Yang, Q., Liu, H., and Qin, B · 2024
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Let’s verify step by step
Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2024
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SimPO: Simple preference optimization with a reference-free reward
Meng, Y., Xia, M., and Chen, D · 2024
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Mistral large, 2024
Mistral · 2024
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Controlled decoding from language models
Mudgal, S., Lee, J., Ganapathy, H., Li, Y., Wang, T., Huang, Y., Chen, Z., Cheng, H.-T., Collins, M., Strohman, T., et al · 2024
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Iterative reasoning preference optimization
Pang, R. Y., Yuan, W., Cho, K., He, H., Sukhbaatar, S., and Weston, J · 2024
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Worledge, T., Shen, J. H., Meister, N., Winston, C., and Guestrin, C · 2023
Cited alongside, same era.
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., Zhang, S., Ghosh, G., Lewis, M., Zettlemoyer, L., and Levy, O · 2023
Cited alongside, same era.
Anthropic: Introducing claude 3.5 sonnet, 2024
Anthropic · 2024
Cited alongside, same era.
Longalign: A recipe for long context alignment of large language models
Bai, Y., Lv, X., Zhang, J., He, Y., Qi, J., Hou, L., Tang, J., Dong, Y., and Li, J · 2024
Cited alongside, same era.
Lookback lens: Detecting and mitigating contextual hallucinations in large language models using only attention maps
Chuang, Y.-S., Qiu, L., Hsieh, C.-Y., Krishna, R., Kim, Y., and Glass, J · 2024
Cited alongside, same era.
Contextcite: Attributing model generation to context
Cohen-Wang, B., Shah, H., Georgiev, K., and Madry, A · 2024
Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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Phukan, A., Somasundaram, S., Saxena, A., Goswami, K., and Srinivasan, B. V · 2024
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Model internals-based answer attribution for trustworthy retrieval-augmented generation
Qi, J., Sarti, G., Fernández, R., and Bisazza, A · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C · 2024
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Trusting your evidence: Hallucinate less with context-aware decoding
Shi, W., Han, X., Lewis, M., Tsvetkov, Y., Zettlemoyer, L., and Yih, W.-t · 2024
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Attribute first, then generate: Locally-attributable grounded text generation
Slobodkin, A., Hirsch, E., Cattan, A., Schuster, T., and Dagan, I · 2024
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Alma: Alignment with minimal annotation
Yasunaga, M., Shamis, L., Zhou, C., Cohen, A., Weston, J., Zettlemoyer, L., and Ghazvininejad, M · 2024
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Self-rewarding language models
Yuan, W., Pang, R. Y., Cho, K., Li, X., Sukhbaatar, S., Xu, J., and Weston, J. E · 2024
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Longcite: Enabling llms to generate fine-grained citations in long-context qa
Zhang, J., Bai, Y., Lv, X., Gu, W., Liu, D., Zou, M., Cao, S., Hou, L., Dong, Y., Feng, L., et al · 2024
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