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Chat-based language models are designed to be helpful, yet they should not comply with every user request.
Epistemic Responsibility
Lorraine Code · 1987
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Making monolingual sentence embeddings multilingual using knowledge distillation
Nils Reimers and Iryna Gurevych · 2004
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An empirical investigation of catastrophic forgetting in gradient-based neural networks, 2015
Ian J. Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2015
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer · 2017
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Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
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Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information
Sudha Rao and Hal Daumé III · 2018
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Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Anatomy of online hate: Developing a taxonomy and machine learning models for identifying and classifying hate in online news media
Joni Salminen, Hind Almerekhi, Milica Milenković, Soon-gyo Jung, Jisun An, Haewoon Kwak, and Bernard Jansen · 2018
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Question answering for privacy policies: Combining computational and legal
Abhilasha Ravichander · 2019
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Asking clarification questions in knowledge-based question answering
Jingjing Xu, Yuechen Wang, Duyu Tang, Nan Duan, Pengcheng Yang, Qi Zeng, Ming Zhou, and Xu Sun · 2019
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A unified taxonomy of harmful content
Michele Banko, Brendon MacKeen, and Laurie Ray · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages
Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki · 2020
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett · 2020
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Evaluating models’ local decision boundaries via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Selective question answering under domain shift
Amita Kamath, Robin Jia, and Percy Liang · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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UNQOVERing stereotyping biases via underspecified questions
Tao Li, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Vivek Srikumar · 2020
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Ambigqa: Answering ambiguous open-domain questions
Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2020
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Challenges in information-seeking QA: Unanswerable questions and paragraph retrieval
Akari Asai and Eunsol Choi · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg · 2021
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How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig · 2021
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Ask what’s missing and what’s useful: Improving clarification question generation using global knowledge
Bodhisattwa Prasad Majumder, Sudha Rao, Michel Galley, and Julian McAuley · 2021
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Creak: A dataset for commonsense reasoning over entity knowledge
Yasumasa Onoe, Michael J.Q. Zhang, Eunsol Choi, and Greg Durrett · 2021
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SituatedQA: Incorporating extra-linguistic contexts into QA
Michael Zhang and Eunsol Choi · 2021
Cited alongside, same era.
Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova Dassarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, John Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom B. Brown, Jack Clark, Sam McCandlish, Christopher Olah, Benjamin Mann, and Jared Kaplan · 2022
Cited alongside, same era.
Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned, 2022
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El-Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom Brown, Nicholas Joseph, Sam McCandlish, Chris Olah, Jared Kaplan, and Jack Clark · 2022
Cited alongside, same era.
Toxigen: Controlling language models to generate implied and adversarial toxicity
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar · 2022
Analyzing leakage of personally identifiable information in language models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella-Béguelin · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Clarifydelphi: Reinforced clarification questions with defeasibility rewards for social and moral situations
Valentina Pyatkin, Jena D Hwang, Vivek Srikumar, Ximing Lu, Liwei Jiang, Yejin Choi, and Chandra Bhagavatula · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2023
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Cited alongside, same era.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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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 · 2022
Cited alongside, same era.
Are large pre-trained language models leaking your personal information?
Jie Huang, Hanyin Shao, and Kevin Chen-Chuan Chang · 2022
Cited alongside, same era.
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, et al · 2022
Cited alongside, same era.
How to approach ambiguous queries in conversational search: A survey of techniques, approaches, tools, and challenges
Kimiya Keyvan and Jimmy Xiangji Huang · 2022
Cited alongside, same era.
Qa 2 : Question answering with questionable assumptions
Najoung Kim, Phu Mon Htut, Samuel R Bowman, and Jackson Petty · 2022
Cited alongside, same era.
Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
Cited alongside, same era.
Reducing conversational agents’ overconfidence through linguistic calibration
Sabrina J Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau · 2022
Cited alongside, same era.
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, et al · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li · 2023
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2023
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CREPE: Open-domain question answering with false presuppositions
Xinyan Yu, Sewon Min, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2023
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Clarify when necessary: Resolving ambiguity through interaction with lms
Michael JQ Zhang and Eunsol Choi · 2023
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Exploring ai ethics of chatgpt: A diagnostic analysis
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing · 2023
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Distinguishing the knowable from the unknowable with language models, 2024
Gustaf Ahdritz, Tian Qin, Nikhil Vyas, Boaz Barak, and Benjamin L. Edelman · 2024
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Introducing the next generation of claude
Anthropic · 2024
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Foundational challenges in assuring alignment and safety of large language models, 2024
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Safety-tuned LLaMAs: Lessons from improving the safety of large language models that follow instructions
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LoRA learns less and forgets less
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Dated data: Tracing knowledge cutoffs in large language models, 2024
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Safe RLHF: Safe reinforcement learning from human feedback
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Don’t hallucinate, abstain: Identifying LLM knowledge gaps via multi-LLM collaboration
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Senate bill no. 1001
California Legislature · 2024
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