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Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications.
Kernel mean embedding of distributions: A review and beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., and Schölkopf, B · 1935
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SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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triviaqa: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., and Zettlemoyer, L · 2017
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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., Kelcey, M., Devlin, J., Lee, K., Toutanova, K. N., Jones, L., Chang, M.-W., Dai, A., Uszkoreit, J., Le, Q., and Petrov, S · 2019
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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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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Language models (mostly) know what they know
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al · 2022
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Factuality enhanced language models for open-ended text generation
Lee, N., Ping, W., Xu, P., Patwary, M., Fung, P. N., Shoeybi, M., and Catanzaro, B · 2022
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Teaching models to express their uncertainty in words
Lin, S., Hilton, J., and Evans, O · 2022
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Reducing conversational agents’ overconfidence through linguistic calibration
Mielke, S. J., Szlam, A., Dinan, E., and Boureau, Y.-L · 2022
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Do language models know when they’re hallucinating references?
Agrawal, A., Suzgun, M., Mackey, L., and Kalai, A. T · 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 · 2023
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Kuhn, L., Gal, Y., and Farquhar, S · 2023
Cited alongside, same era.
Generating with confidence: Uncertainty quantification for black-box large language models
Zhang, J., Li, Z., Das, K., Malin, B., and Kumar, S · 2023
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Representation engineering: A top-down approach to ai transparency
Zou, A., Phan, L., Chen, S., Campbell, J., Guo, P., Ren, R., Pan, A., Yin, X., Mazeika, M., Dombrowski, A.-K., et al · 2023
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Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models
Duan, J., Cheng, H., Wang, S., Zavalny, A., Wang, C., Xu, R., Kailkhura, B., and Xu, K · 2024
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Detecting hallucinations in large language models using semantic entropy
Farquhar, S., Kossen, J., Kuhn, L., and Gal, Y · 2024
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Lin, Z., Trivedi, S., and Sun, J · 2023
Cited alongside, same era.
Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Manakul, P., Liusie, A., and Gales, M. J · 2023
Cited alongside, same era.
Varshney, N., Yao, W., Zhang, H., Chen, J., and Yu, D · 2023
Cited alongside, same era.
Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Xiong, M., Hu, Z., Lu, X., Li, Y., Fu, J., He, J., and Hooi, B · 2023
Cited alongside, same era.
Fine-tuning language models for factuality
Tian, K., Mitchell, E., Yao, H., Manning, C. D., and Finn, C
Cited in the paper.
Tian, K., Mitchell, E., Zhou, A., Sharma, A., Rafailov, R., Yao, H., Finn, C., and Manning, C. D
Cited in the paper.
Kalai, A. T. and Vempala, S. S · 2024
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Inference-time intervention: Eliciting truthful answers from a language model
Li, K., Patel, O., Viégas, F., Pfister, H., and Wattenberg, M · 2024
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Kernel language entropy: Fine-grained uncertainty quantification for llms from semantic similarities
Nikitin, A., Kossen, J., Gal, Y., and Marttinen, P · 2024
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Characterizing truthfulness in large language model generations with local intrinsic dimension
Yin, F., Srinivasa, J., and Chang, K.-W · 2024
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