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Consistency is a fundamental dimension of trustworthiness in Large Language Models (LLMs).
Deberta: Decoding-enhanced bert with disentangled attention, 2020
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen · 2006
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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A broad-coverage challenge corpus for sentence understanding through inference, 2017
Adina Williams, Nikita Nangia, and Samuel R. Bowman · 2017
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering, 2018
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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ELI5: Long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli · 2019
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Relational knowledge distillation
Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2019
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PAWS: Paraphrase adversaries from word scrambling
Yuan Zhang, Jason Baldridge, and Luheng He · 2019
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Ambigqa: Answering ambiguous open-domain questions, 2020
Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2020
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Measuring and Improving Consistency in Pretrained Language Models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, et al · 2021
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Pengcheng He, Jianfeng Gao, and Weizhu Chen · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Accurate, yet inconsistent? consistency analysis on language understanding models
Myeongjun Jang, Deuk Sin Kwon, and Thomas Lukasiewicz · 2021
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BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief
Nora Kassner, Oyvind Tafjord, Hinrich Schütze, and Peter Clark · 2021
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Prompt consistency for zero-shot task generalization, 2022
Chunting Zhou, Junxian He, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2021
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Constitutional ai: Harmlessness from ai feedback, 2022
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan · 2022
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Mart: Improving llm safety with multi-round automatic red-teaming, 2023
Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, and Yuning Mao · 2023
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Survey on sociodemographic bias in natural language processing
Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson, and Rebecca J Passonneau · 2023
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Dlama: A framework for curating culturally diverse facts for probing the knowledge of pretrained language models, 2023
Amr Keleg and Walid Magdy · 2023
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Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation, 2023
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar · 2023
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Rlaif: Scaling reinforcement learning from human feedback with ai feedback, 2023
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash · 2023
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Factual Consistency of Multilingual Pretrained Language Models
Constanza Fierro and Anders Søgaard · 2022
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Auto-debias: Debiasing masked language models with automated biased prompts
Yue Guo, Yi Yang, and Ahmed Abbasi · 2022
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TruthfulQA: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Enhancing self-consistency and performance of pre-trained language models through natural language inference
Eric Mitchell, Joseph Noh, Siyan Li, Will Armstrong, Ananth Agarwal, Patrick Liu, Chelsea Finn, and Christopher Manning · 2022
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Benjamin Newman, Prafulla Kumar Choubey, and Nazneen Rajani · 2022
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Measuring reliability of large language models through semantic consistency, 2022
Harsh Raj, Domenic Rosati, and Subhabrata Majumdar · 2022
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Unpacking large language models with conceptual consistency, 2022
Pritish Sahu, Michael Cogswell, Yunye Gong, and Ajay Divakaran · 2022
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Evaluating the factual consistency of large language models through summarization, 2022
Derek Tam, Anisha Mascarenhas, Shiyue Zhang, Sarah Kwan, Mohit Bansal, and Colin Raffel · 2022
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Predicting question-answering performance of large language models through semantic consistency
Ella Rabinovich, Samuel Ackerman, Orna Raz, Eitan Farchi, and Ateret Anaby Tavor · 2023
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Prompting gpt-3 to be reliable, 2023
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang · 2023
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Chain-of-thought prompting elicits reasoning in large language models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 2023
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Alignment studio: Aligning large language models to particular contextual regulations, 2024
Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf, Joan Byamugisha, Maria Chang, Pierre Dognin, Eitan Farchi, Ndivhuwo Makondo, Aleksandra Mojsilovic, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Inkit Padhi, Orna Raz, Jesus Rios, Prasanna Sattigeri, Moninder Singh, Siphiwe Thwala, Rosario A. Uceda-Sosa, and Kush R. Varshney · 2024
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Language models are homer simpson! safety re-alignment of fine-tuned language models through task arithmetic, 2024
Rishabh Bhardwaj, Do Duc Anh, and Soujanya Poria · 2024
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Suppressing pink elephants with direct principle feedback, 2024
Louis Castricato, Nathan Lile, Suraj Anand, Hailey Schoelkopf, Siddharth Verma, and Stella Biderman · 2024
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Rainbow teaming: Open-ended generation of diverse adversarial prompts, 2024
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Tim Rocktäschel, and Roberta Raileanu · 2024
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Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang · 2024
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Two-stage llm fine-tuning with less specialization and more generalization, 2024
Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, and Sanjiv Kumar · 2024
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Reft: Representation finetuning for language models, 2024
Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D. Manning, and Christopher Potts · 2024
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