2 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning
Rahul Markasserithodi, Aditya Joshi, Yuekang Li +3
Despite advances in safety alignment, prompt-rewriting attacks such as persona modulation, fictional framing and persuasion-based reformulation, can bypass safety filters even on f…
Debiasing LLMs by Masking Unfairness-Driving Attention Heads
Tingxu Han, Wei Song, Ziqi Ding +6
Large language models (LLMs) increasingly mediate decisions in domains where unfair treatment of demographic groups is unacceptable. Existing work probes when biased outputs appear…
"Pull or Not to Pull?'': Investigating Moral Biases in Leading Large Language Models Across Ethical Dilemmas
Junchen Ding, Penghao Jiang, Zihao Xu +4
As large language models (LLMs) increasingly mediate ethically sensitive decisions, understanding their moral reasoning processes becomes imperative. This study presents a comprehe…
Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles
Zihao Xu, Junchen Ding, Yiling Lou +3
Large Language Models (LLMs) have achieved significant progress in language understanding and reasoning. Evaluating and analyzing their logical reasoning abilities has therefore be…