Securing the Future of GenAI: Policy and Technology

no code implementations • 21 May 2024 • Mihai Christodorescu, Ryan Craven, Soheil Feizi, Neil Gong, Mia Hoffmann, Somesh Jha, Zhengyuan Jiang, Mehrdad Saberi Kamarposhti, John Mitchell, Jessica Newman, Emelia Probasco, Yanjun Qi, Khawaja Shams, Matthew Turek

The interplay between legislation and technology is a very vast topic, and we don't claim that this paper is a comprehensive treatment on this topic.

Social Skill Training with Large Language Models

no code implementations • 5 Apr 2024 • Diyi Yang, Caleb Ziems, William Held, Omar Shaikh, Michael S. Bernstein, John Mitchell

People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life.

DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training

no code implementations • 5 Mar 2024 • ZiHao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang, John Mitchell, Haixu Tang, XiaoFeng Wang

DPAdapter modifies and enhances the sharpness-aware minimization (SAM) technique, utilizing a two-batch strategy to provide a more accurate perturbation estimate and an efficient gradient descent, thereby improving parameter robustness against noise.

TrustLLM: Trustworthiness in Large Language Models

1 code implementation • 10 Jan 2024 • Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong Huang, Hao liu, Heng Ji, Hongyi Wang, huan zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao

This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.

Ethics Fairness

Identifying and Mitigating the Security Risks of Generative AI

no code implementations • 28 Aug 2023 • Clark Barrett, Brad Boyd, Elie Burzstein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, Kathleen Fisher, Tatsunori Hashimoto, Dan Hendrycks, Somesh Jha, Daniel Kang, Florian Kerschbaum, Eric Mitchell, John Mitchell, Zulfikar Ramzan, Khawaja Shams, Dawn Song, Ankur Taly, Diyi Yang

However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks.

Code Completion In-Context Learning +1

Cooperative coinfection dynamics on clustered networks

no code implementations • 17 Dec 2020 • Peter Mann, Anne Smith, John Mitchell, Simon Dobson

Coinfection is the process by which a host that is infected with a pathogen becomes infected by a second pathogen at a later point in time.

Physics and Society Populations and Evolution

Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems

1 code implementation • 23 May 2019 • Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, Madison Coots, John Mitchell, Noah Goodman, Chris Piech

In this paper, we present generative grading: a novel computational approach for providing feedback at scale that is capable of accurately grading student work and providing nuanced, interpretable feedback.