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Large language models (LLMs) can suffer from hallucinations when generating text.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
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Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
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Efficient estimation of word representations in vector space
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Sequence to sequence learning with neural networks
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Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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A diversity-promoting objective function for neural conversation models
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Hafez: an interactive poetry generation system
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Sanity checks for saliency maps
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Learning to write with cooperative discriminators
Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub, and Yejin Choi · 2018
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Diverse beam search: Decoding diverse solutions from neural sequence models
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A broad-coverage challenge corpus for sentence understanding through inference
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Comparison of diverse decoding methods from conditional language models
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CTRL: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher · 2019
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning · 2019
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu · 2020
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
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Exploring controllable text generation techniques
Shrimai Prabhumoye, Alan W Black, and Ruslan Salakhutdinov · 2020
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Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P. Parikh · 2020
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Cluster-based beam search for pointer-generator chatbot grounded by knowledge
Yik-Cheung Tam · 2020
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Cocon: A self-supervised approach for controlled text generation
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Deberta: Decoding-enhanced bert with disentangled attention
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A survey of uncertainty in deep neural networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, and Xiao Xiang Zhu · 2023
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News summarization and evaluation in the era of gpt-3
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Decomposing uncertainty for large language models through input clarification ensembling
Bairu Hou, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang, and Yang Zhang · 2023
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Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar · 2023
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Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis · 2023
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
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Uncertainty estimation in autoregressive structured prediction
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On hallucination and predictive uncertainty in conditional language generation
Yijun Xiao and William Yang Wang · 2021
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Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan · 2022
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Post-hoc interpretability for neural nlp: A survey
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Reducing conversational agents’ overconfidence through linguistic calibration
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Generating with confidence: Uncertainty quantification for black-box large language models
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Conformal language modeling
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Self-evaluation improves selective generation in large language models
Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, and Balaji Lakshminarayanan · 2023
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Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
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A survey of controllable text generation using transformer-based pre-trained language models
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song · 2023
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Navigating the grey area: How expressions of uncertainty and overconfidence affect language models
Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto · 2023
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Mars: Meaning-aware response scoring for uncertainty estimation in generative llms
Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Chenyang Tao, Dimitrios Dimitriadis, and Salman Avestimehr · 2024
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Inside: Llms’ internal states retain the power of hallucination detection, 2024
Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, Mingyuan Tao, Zhihang Fu, and Jieping Ye · 2024
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Detecting hallucinations in large language models using semantic entropy
Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, and Yarin Gal · 2024
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Kernel language entropy: Fine-grained uncertainty quantification for llms from semantic similarities
Alexander Nikitin, Jannik Kossen, Yarin Gal, and Pekka Marttinen · 2024
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