9 citations · 28 across the 13 of their papers we have counts for
10 papers · 1 filter
Robust Multi-bit Text Watermark with LLM-based Paraphrasers
Xiaojun Xu, Jinghan Jia, Yuanshun Yao +2
We propose an imperceptible multi-bit text watermark embedded by paraphrasing with LLMs. We fine-tune a pair of LLM paraphrasers that are designed to behave differently so that the…
ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration
Andrew Estornell, Jean-Francois Ton, Yuanshun Yao +1
Large language models (LLMs) have demonstrated a remarkable ability to serve as general-purpose tools for various language-based tasks. Recent works have demonstrated that the effi…
Toward Optimal LLM Alignments Using Two-Player Games
Rui Zheng, Hongyi Guo, Zhihan Liu +10
The standard Reinforcement Learning from Human Feedback (RLHF) framework primarily focuses on optimizing the performance of large language models using pre-collected prompts. Howev…
Label Smoothing Improves Machine Unlearning
Zonglin Di, Zhaowei Zhu, Jinghan Jia +6
The objective of machine unlearning (MU) is to eliminate previously learned data from a model. However, it is challenging to strike a balance between computation cost and performan…
Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards
Wei Shen, Xiaoying Zhang, Yuanshun Yao +3
Reinforcement learning from human feedback (RLHF) is the mainstream paradigm used to align large language models (LLMs) with human preferences. Yet existing RLHF heavily relies on…
Learning to Watermark LLM-generated Text via Reinforcement Learning
Xiaojun Xu, Yuanshun Yao, Yang Liu
We study how to watermark LLM outputs, i.e. embedding algorithmically detectable signals into LLM-generated text to track misuse. Unlike the current mainstream methods that work wi…
Fairness Without Harm: An Influence-Guided Active Sampling Approach
Jinlong Pang, Jialu Wang, Zhaowei Zhu +3
The pursuit of fairness in machine learning (ML), ensuring that the models do not exhibit biases toward protected demographic groups, typically results in a compromise scenario. Th…
Measuring and Reducing LLM Hallucination without Gold-Standard Answers
Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton +3
LLM hallucination, i.e. generating factually incorrect yet seemingly convincing answers, is currently a major threat to the trustworthiness and reliability of LLMs. The first step…
Rethinking Machine Unlearning for Large Language Models
Sijia Liu, Yuanshun Yao, Jinghan Jia +11
We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence (e.g.,…
Human-Instruction-Free LLM Self-Alignment with Limited Samples
Hongyi Guo, Yuanshun Yao, Wei Shen +4
Aligning large language models (LLMs) with human values is a vital task for LLM practitioners. Current alignment techniques have several limitations: (1) requiring a large amount o…