Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Columbia University（哥伦比亚大学）；Princeton University（普林斯顿大学）
Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel
备注：
34 pages, 10 figures, published as conference paper at ICLR 2024, and accepted to the Socially Responsible Language Modelling Research (SoLaR) workshop at NeurIPS 2023
The recent explosion in the capabilities of large language models has led to a wave of interest in how best to prompt a model to perform a given task. While it may be tempting to simply choose a prompt based on average performance on a validation set, this can lead to a deployment where unexpectedly poor responses are generated, especially for the worst-off users. To mitigate this prospect, we propose Prompt Risk Control, a lightweight framework for selecting a prompt based on rigorous upper bounds on families of informative risk measures. We offer methods for producing bounds on a diverse set of metrics, including quantities that measure worst-case responses and disparities in generation quality across the population of users. In addition, we extend the underlying statistical bounding techniques to accommodate the possibility of distribution shifts in deployment. Experiments on applications such as open-ended chat, medical question summarization, and code generation highlight how such a framework can foster responsible deployment by reducing the risk of the worst outcomes.
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
MIT（麻省理工学院）；EleutherAI；UCSB（加利福尼亚大学圣巴巴拉分校）；SynthLabs；Princeton University（普林斯顿大学）；Stanford University（斯坦福大学）；Harvard University（哈佛大学）；Allen Institute for AI（艾伦人工智能研究所）；Google DeepMind（谷歌DeepMind）；ML Commons；Contextual AI；HuggingFace；University College London（伦敦大学学院）
Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Saarland Informatics Campus, Germany（萨尔兰州信息学校区，德国）；Center for European Research in Trusted AI (CERTAIN)（可信人工智能欧洲研究中心（CERTAIN））
Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)
School of Social Work, University of Michigan（密歇根大学社会工作学院）；Graduate School of Social Service, Fordham University（福特汉姆大学社会工作研究生院）；School of Social Work, Wayne State University（韦恩州立大学社会工作学院）
An investigation of structures responsible for gender bias in BERT and DistilBERT
Université Jean Monnet Saint-Étienne（圣艾蒂安让·莫内大学）；CNRS（法国国家科学研究中心）；Laboratoire Hubert Curien UMR 5516（于贝尔·居里安实验室）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
IDEA Research（IDEA研究院）；International Digital Economy Academy（国际数字经济学院）；Xiamen University（厦门大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Microsoft Research Asia（微软亚洲研究院）；University of Southern California（南加州大学）；The Hong Kong University of Science and Technology（香港科技大学）
RefactorCoderQA: Benchmarking LLMs for Multi-Domain Coding Question Solutions in Cloud and Edge Deployment
York University and Algoma University（约克大学和阿尔戈马大学）；Algoma University（阿尔戈马大学）；Department of Systems and Computer Engineering, Carleton University（系统与计算机工程系，卡尔顿大学）；Department of Math and Computer Science, Brandon University（数学与计算机科学系，布兰登大学）
Responsible Machine Learning via Mixed-Integer Optimization
Center for Artificial Intelligence in Society（人工智能与社会中心）；Department of Computer Science（计算机科学系）；University of Southern California（南加州大学）；Department of Industrial & Systems Engineering（工业与系统工程系）
A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance
Deparment of Computer Science and Artificial Intelligence and Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada（计算机科学与人工智能系和安达卢西亚数据科学与计算智能研究所（DaSCI），格拉纳达大学）；TECNALIA (BRTA)（TECNALIA（BRTA））；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；School of Engineering, Cornell University（工程学院，康奈尔大学）；ADIA Lab（ADIA实验室）；Computational Research Department, Lawrence Berkeley National Laboratory（计算研究部，劳伦斯伯克利国家实验室）；Intelligent Systems for Robotics and Automation Laboratory, Macau University of Science and Technology（机器人与自动化智能系统实验室，澳门科学技术大学）
Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity
New York University（纽约大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Simon Fraser University（西蒙弗雷泽大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
