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Quantifying uncertainty in automatically generated text is important for letting humans check potential hallucinations and making systems more reliable.
Speech understanding systems: Report of a steering committee
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Kadan Lottick, Silvia Susai, Sorelle A. Friedler, and Jonathan P. Wilson. 2019 · 2019
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Conformal prediction under covariate shift
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Is MAP decoding all you need? the inadequacy of the mode in neural machine translation
Bryan Eikema and Wilker Aziz. 2020 · 2020
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
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Generalization through memorization: Nearest neighbor language models
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Uncertainty sets for image classifiers using conformal prediction
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Neil Dey, Jing Ding, Jack Ferrell, Carolina Kapper, Maxwell Lovig, Emiliano Planchon, and Jonathan P Williams. 2021 · 2021
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Opt: Open pre-trained transformer language models
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Artificial hallucinations in chatgpt: implications in scientific writing
Hussam Alkaissi and Samy I McFarlane. 2023 · 2023
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Large language models and the perils of their hallucinations
Razvan Azamfirei, Sapna R Kudchadkar, and James Fackler. 2023 · 2023
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Uncertainty in natural language generation: From theory to applications
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Conformal prediction beyond exchangeability
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Conformal autoregressive generation: Beam search with coverage guarantees
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Evaluating machine translation quality with conformal predictive distributions
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Localized conformal prediction: A generalized inference framework for conformal prediction
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Looking for a needle in a haystack: A comprehensive study of hallucinations in neural machine translation
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Survey of hallucination in natural language generation
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Conformal prediction with large language models for multi-choice question answering
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Locally typical sampling
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OpenAI. 2023 · 2023
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