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Large language models (LLMs) can be prone to hallucinations - generating unreliable outputs that are unfaithful to their inputs, external facts or internally inconsistent.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
Earlier work this paper cites.
Perplexity—a measure of the difficulty of speech recognition tasks
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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Earlier work this paper cites.
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Earlier work this paper cites.
On faithfulness and factuality in abstractive summarization
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Deberta: Decoding-enhanced bert with disentangled attention
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A token-level reference-free hallucination detection benchmark for free-form text generation
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Retrieval augmentation reduces hallucination in conversation
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On hallucination and predictive uncertainty in conditional language generation
Yijun Xiao and William Yang Wang · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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Generating with confidence: Uncertainty quantification for black-box large language models
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Halueval: A large-scale hallucination evaluation benchmark for large language models
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Falcon-40B: an open large language model with state-of-the-art performance
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Angle-optimized text embeddings
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Calibrated language models must hallucinate
Adam Tauman Kalai and Santosh S Vempala · 2023
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Multicalibration for confidence scoring in llms
Gianluca Detommaso, Martin Bertran, Riccardo Fogliato, and Aaron Roth · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Exploring and evaluating hallucinations in llm-powered code generation
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