4 citations · 8 across the 5 of their papers we have counts for
5 papers
Reasoning Robustness of LLMs to Adversarial Typographical Errors
Esther Gan, Yiran Zhao, Liying Cheng +5
Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning using Chain-of-Thought (CoT) prompting. However, CoT can be biased by users' instruction. In thi…
Self-Evaluation as a Defense Against Adversarial Attacks on LLMs
Hannah Brown, Leon Lin, Kenji Kawaguchi +1
We introduce a defense against adversarial attacks on LLMs utilizing self-evaluation. Our method requires no model fine-tuning, instead using pre-trained models to evaluate the inp…
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language Models
Hongfu Liu, Yuxi Xie, Ye Wang +1
Language Language Models (LLMs) face safety concerns due to potential misuse by malicious users. Recent red-teaming efforts have identified adversarial suffixes capable of jailbrea…
Single Character Perturbations Break LLM Alignment
Leon Lin, Hannah Brown, Kenji Kawaguchi +1
When LLMs are deployed in sensitive, human-facing settings, it is crucial that they do not output unsafe, biased, or privacy-violating outputs. For this reason, models are both tra…
Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
Yuxi Xie, Anirudh Goyal, Wenyue Zheng +4
We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process inspired by the successful s…