9 citations · 16 across the 9 of their papers we have counts for
9 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
I Know Who Clones Your Code: Interpretable Smart Contract Similarity Detection
Zhenguang Liu, Lixun Ma, Zhongzheng Mu +4
Widespread reuse of open-source code in smart contract development boosts programming efficiency but significantly amplifies bug propagation across contracts, while dedicated metho…
Reasoned Safety Alignment: Ensuring Jailbreak Defense via Answer-Then-Check
Chentao Cao, Xiaojun Xu, Bo Han +1
As large language models (LLMs) continue to advance in capabilities, ensuring their safety against jailbreak attacks remains a critical challenge. In this paper, we introduce a nov…
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning
Han Zhong, Yutong Yin, Shenao Zhang +10
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, yet generating reliable reasoning processes remains a significant challenge. We p…
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…
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…
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…
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.,…
Large Language Model Unlearning
Yuanshun Yao, Xiaojun Xu, Yang Liu
We study how to perform unlearning, i.e. forgetting undesirable misbehaviors, on large language models (LLMs). We show at least three scenarios of aligning LLMs with human preferen…