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Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge.
Eli5: Long form question answering
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Generalized entropy regularization or: There’s nothing special about label smoothing
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Entropy and information theory
Gray, R. M. (2011) · 2011
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T-rex: A large scale alignment of natural language with knowledge base triples
Elsahar, H., Vougiouklis, P., Remaci, A., Gravier, C., Hare, J., Laforest, F., and Simperl, E. (2018) · 2018
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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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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
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Wizard of Wikipedia: Knowledge-powered conversational agents
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., and Weston, J. (2019) · 2019
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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., et al. (2019) · 2019
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Multi-passage BERT: A globally normalized BERT model for open-domain question answering
Wang, Z., Ng, P., Ma, X., Nallapati, R., and Xiang, B. (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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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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Leveraging passage retrieval with generative models for open domain question answering
Izacard, G. and Grave, E. (2021) · 2021
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Entity-based knowledge conflicts in question answering
Longpre, S., Perisetla, K., Chen, A., Ramesh, N., DuBois, C., and Singh, S. (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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On homophony and r \ \backslash ’enyi entropy
Pimentel, T., Meister, C., Teufel, S., and Cotterell, R. (2021) · 2021
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Colbertv2: Effective and efficient retrieval via lightweight late interaction
Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., and Zaharia, M. (2021) · 2021
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Machine translationese: Effects of algorithmic bias on linguistic complexity in machine translation
Vanmassenhove, E., Shterionov, D., and Gwilliam, M. (2021) · 2021
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Contrastive decoding: Open-ended text generation as optimization
Li, X. L., Holtzman, A., Fried, D., Liang, P., Eisner, J., Hashimoto, T., Zettlemoyer, L., and Lewis, M. (2022) · 2022
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Locating and editing factual associations in gpt
Meng, K., Bau, D., Andonian, A., and Belinkov, Y. (2022) · 2022
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Mteb: Massive text embedding benchmark
Muennighoff, N., Tazi, N., Magne, L., and Reimers, N. (2022) · 2022
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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., Jiang, X., Cobbe, K., Eloundou, T., Krueger, G., Button, K., Knight, M., Chess, B., and Schulman, J. (2022) · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. (2023) · 2023
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Detecting language model attacks with perplexity
Alon, G. and Kamfonas, M. (2023) · 2023
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Dola: Decoding by contrasting layers improves factuality in large language models
Gtbench: Uncovering the strategic reasoning limitations of llms via game-theoretic evaluations
Duan, J., Zhang, R., Diffenderfer, J., Kailkhura, B., Sun, L., Stengel-Eskin, E., Bansal, M., Chen, T., and Xu, K. (2024) · 2024
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A survey on rag meeting llms: Towards retrieval-augmented large language models
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., and Li, Q. (2024) · 2024
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Alphaedit: Null-space constrained knowledge editing for language models
Fang, J., Jiang, H., Wang, K., Ma, Y., Wang, X., He, X., and Chua, T.-s. (2024) · 2024
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Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · 2024
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Chuang, Y.-S., Xie, Y., Luo, H., Kim, Y., Glass, J., and He, P. (2023) · 2023
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Duan, J., Cheng, H., Wang, S., Zavalny, A., Wang, C., Xu, R., Kailkhura, B., and Xu, K. (2023) · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, H., and Wang, H. (2023) · 2023
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A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., and Liu, T. (2023) · 2023
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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. (2023b) · 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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Fine-tuning language models for factuality
Tian, K., Mitchell, E., Yao, H., Manning, C. D., and Finn, C. (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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Gunjal, A., Yin, J., and Bas, E. (2024) · 2024
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Retrieval augmented generation or long-context llms? a comprehensive study and hybrid approach
Li, Z., Li, C., Zhang, M., Mei, Q., and Bendersky, M. (2024b) · 2024
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Exploring and evaluating hallucinations in llm-powered code generation
Liu, F., Liu, Y., Shi, L., Huang, H., Wang, R., Yang, Z., and Zhang, L. (2024) · 2024
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Hallucination detection and hallucination mitigation: An investigation
Luo, J., Li, T., Wu, D., Jenkin, M., Liu, S., and Dudek, G. (2024) · 2024
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Tool learning with foundation models
Qin, Y., Hu, S., Lin, Y., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y., Xiao, C., Han, C., Fung, Y. R., Su, Y., Wang, H., Qian, C., Tian, R., Zhu, K., Liang, S., Shen, X., Xu, B., Zhang, Z., Ye, Y., Li, B., Tang, Z., Yi, J., Zhu, Y., Dai, Z., Yan, L., Cong, X., Lu, Y., Zhao, W., Huang, Y., Yan, J., Han, X., Sun, X., Li, D., Phang, J., Yang, C., Wu, T., Ji, H., Liu, Z., and Sun, M. (2024) · 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) · 2024
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A comprehensive survey of hallucination mitigation techniques in large language models
Tonmoy, S. M. T. I., Zaman, S. M. M., Jain, V., Rani, A., Rawte, V., Chadha, A., and Das, A. (2024) · 2024
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Wang, F., Wan, X., Sun, R., Chen, J., and Arık, S. Ö. (2024) · 2024
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Instructrag: Instructing retrieval-augmented generation via self-synthesized rationales
Wei, Z., Chen, W.-L., and Meng, Y. (2024) · 2024
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Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Li, C., Liu, D., Huang, F., Wei, H., et al. (2024) · 2024
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Reinforced lifelong editing for language models
Li, Z., Jiang, H., Chen, H., Bi, B., Zhou, Z., Sun, F., Fang, J., and Wang, X. (2025) · 2025
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Estimating llm uncertainty with logits
Ma, H., Chen, J., Wang, G., and Zhang, C. (2025) · 2025
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Towards fully exploiting llm internal states to enhance knowledge boundary perception
Ni, S., Bi, K., Guo, J., Yu, L., Bi, B., and Cheng, X. (2025) · 2025
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Ircan: Mitigating knowledge conflicts in llm generation via identifying and reweighting context-aware neurons
Shi, D., Jin, R., Shen, T., Dong, W., Wu, X., and Xiong, D. (2025) · 2025
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Explainable and efficient editing for large language models
Zhang, T., Fang, J., Jiang, H., Bi, B., Wang, X., and He, X. (2025) · 2025
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