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The rapid progress in Large Language Models (LLMs) poses potential risks such as generating unethical content.
Modern hierarchical, agglomerative clustering algorithms
Daniel Müllner · 2011
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An overview of the schwartz theory of basic values
Shalom H Schwartz · 2012
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Culture matters: National value cultures, sources, and consequences
Shalom H Schwartz · 2013
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Moral foundations theory: The pragmatic validity of moral pluralism
Jesse Graham, Jonathan Haidt, Sena Koleva, Matt Motyl, Ravi Iyer, Sean P Wojcik, and Peter H Ditto · 2013
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Personal values in human life
Lilach Sagiv, Sonia Roccas, Jan Cieciuch, and Shalom H Schwartz · 2017
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Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith · 2020
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Aligning ai with shared human values
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt · 2020
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Moral stories: Situated reasoning about norms, intents, actions, and their consequences
Denis Emelin, Ronan Le Bras, Jena D Hwang, Maxwell Forbes, and Yejin Choi · 2020
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A cross-cultural examination on global orientations and moral foundations
Xiaomeng Hu, Yijie Zhu, Feng Yu, David A Wilder, Li Zhang, Sylvia Xiaohua Chen, and Kaiping Peng · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy · 2021
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Can machines learn morality? the delphi experiment
Liwei Jiang, Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, et al · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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Bold: Dataset and metrics for measuring biases in open-ended language generation
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta · 2021
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Bbq: A hand-built bias benchmark for question answering
Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R Bowman · 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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On the opportunities and risks of foundation models, 2022
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2022
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Moral foundations of large language models
Marwa Abdulhai, Clément Crepy, Daria Valter, John Canny, and Natasha Jaques · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Valuenet: A new dataset for human value driven dialogue system
Liang Qiu, Yizhou Zhao, Jinchao Li, Pan Lu, Baolin Peng, Jianfeng Gao, and Song-Chun Zhu · 2022
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Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Gemini: A family of highly capable multimodal models, 2023
Gemini Team · 2023
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Mistral 7b, 2023
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
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On second thought, let’s not think step by step! bias and toxicity in zero-shot reasoning
Omar Shaikh, Hongxin Zhang, William Held, Michael Bernstein, and Diyi Yang · 2023
Cited alongside, same era.
Red-teaming large language models using chain of utterances for safety-alignment, 2023
Rishabh Bhardwaj and Soujanya Poria · 2023
Cited alongside, same era.
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, et al · 2023
Cited alongside, same era.
Holistic evaluation of language models
Rishi Bommasani, Percy Liang, and Tony Lee · 2023
Cited alongside, same era.
Inverse scaling: When bigger isn’t better
Ian R McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, et al · 2023
Large language models are diverse role-players for summarization evaluation
Ning Wu, Ming Gong, Linjun Shou, Shining Liang, and Daxin Jiang · 2023
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Chateval: Towards better llm-based evaluators through multi-agent debate
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu · 2023
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Prd: Peer rank and discussion improve large language model based evaluations
Ruosen Li, Teerth Patel, and Xinya Du · 2023
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Hosein Hasanbeig, Hiteshi Sharma, Leo Betthauser, Felipe Vieira Frujeri, and Ida Momennejad · 2023
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Openassistant conversations-democratizing large language model alignment
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Cited alongside, same era.
Josh A Goldstein, Girish Sastry, Micah Musser, Renee DiResta, Matthew Gentzel, and Katerina Sedova · 2023
Cited alongside, same era.
Value fulcra: Mapping large language models to the multidimensional spectrum of basic human values
Jing Yao, Xiaoyuan Yi, Xiting Wang, Yifan Gong, and Xing Xie · 2023
Cited alongside, same era.
Assessing cross-cultural alignment between chatgpt and human societies: An empirical study
Yong Cao, Li Zhou, Seolhwa Lee, Laura Cabello, Min Chen, and Daniel Hershcovich · 2023
Cited alongside, same era.
Safetybench: Evaluating the safety of large language models with multiple choice questions
Zhexin Zhang, Leqi Lei, Lindong Wu, Rui Sun, Yongkang Huang, Chong Long, Xiao Liu, Xuanyu Lei, Jie Tang, and Minlie Huang · 2023
Cited alongside, same era.
From values to opinions: Predicting human behaviors and stances using value-injected large language models
Dongjun Kang, Joonsuk Park, Yohan Jo, and JinYeong Bak · 2023
Cited alongside, same era.
Cvalues: Measuring the values of chinese large language models from safety to responsibility
Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, et al · 2023
Cited alongside, same era.
Heterogeneous value evaluation for large language models
Zhaowei Zhang, Nian Liu, Siyuan Qi, Ceyao Zhang, Ziqi Rong, Yaodong Yang, and Shuguang Cui · 2023
Cited alongside, same era.
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Richárd Nagyfi, et al · 2023
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Pei Ke, Bosi Wen, Zhuoer Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, et al · 2023
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Minqian Liu, Ying Shen, Zhiyang Xu, Yixin Cao, Eunah Cho, Vaibhav Kumar, Reza Ghanadan, and Lifu Huang · 2023
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Shepherd: A critic for language model generation
Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan, Sean O’Brien, Ramakanth Pasunuru, Jane Dwivedi-Yu, Olga Golovneva, Luke Zettlemoyer, Maryam Fazel-Zarandi, and Asli Celikyilmaz · 2023
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Trueteacher: Learning factual consistency evaluation with large language models
Zorik Gekhman, Jonathan Herzig, Roee Aharoni, Chen Elkind, and Idan Szpektor · 2023
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Frugalgpt: How to use large language models while reducing cost and improving performance
Lingjiao Chen, Matei Zaharia, and James Zou · 2023
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Wider and deeper llm networks are fairer llm evaluators
Xinghua Zhang, Bowen Yu, Haiyang Yu, Yangyu Lv, Tingwen Liu, Fei Huang, Hongbo Xu, and Yongbin Li · 2023
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Gpt-4 technical report, 2024
OpenAI · 2024
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Managing extreme ai risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, et al · 2024
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Measuring implicit bias in explicitly unbiased large language models
Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L Griffiths · 2024
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Do-not-answer: Evaluating safeguards in LLMs
Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, and Timothy Baldwin · 2024
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Inadequacies of large language model benchmarks in the era of generative artificial intelligence
Timothy R McIntosh, Teo Susnjak, Tong Liu, Paul Watters, and Malka N Halgamuge · 2024
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Evaluating the moral beliefs encoded in llms
Nino Scherrer, Claudia Shi, Amir Feder, and David Blei · 2024
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al · 2024
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Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, and Tiejun Zhao · 2024
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Trustllm: Trustworthiness in large language models
Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, et al · 2024
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Evaluating the moral beliefs encoded in llms
Nino Scherrer, Claudia Shi, Amir Feder, and David Blei · 2024
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A comprehensive analysis of the effectiveness of large language models as automatic dialogue evaluators
Chen Zhang, Luis Fernando D’Haro, Yiming Chen, Malu Zhang, and Haizhou Li · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
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Rewardbench: Evaluating reward models for language modeling
Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, et al · 2024
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Optimising calls to large language models with uncertainty-based two-tier selection
Guillem Ramírez, Alexandra Birch, and Ivan Titov · 2024
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Hybrid llm: Cost-efficient and quality-aware query routing
Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Ruhle, Laks VS Lakshmanan, and Ahmed Hassan Awadallah · 2024
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Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al · 2024
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