4 citations · 4 across the 5 of their papers we have counts for
5 papers
Jailbreaking Large Language Models with Morality Attacks
Ying Su, Mingen Zheng, Weili Diao +1
Pluralism alignment with AI has the sophisticated and necessary goal of creating AI that can coexist with and serve morally multifaceted humanity. Research towards pluralism alignm…
ActPlan-1K: Benchmarking the Procedural Planning Ability of Visual Language Models in Household Activities
Ying Su, Zhan Ling, Haochen Shi +3
Large language models~(LLMs) have been adopted to process textual task description and accomplish procedural planning in embodied AI tasks because of their powerful reasoning abili…
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
Qineng Wang, Zihao Wang, Ying Su +2
Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experime…
On the Discussion of Large Language Models: Symmetry of Agents and Interplay with Prompts
Qineng Wang, Zihao Wang, Ying Su +1
Two ways has been discussed to unlock the reasoning capability of a large language model. The first one is prompt engineering and the second one is to combine the multiple inferenc…
Are LLMs Rigorous Logical Reasoners? Empowering Natural Language Proof Generation by Stepwise Decoding with Contrastive Learning
Ying Su, Mingwen Liu, Zhijiang Guo
Logical reasoning is a pivotal component in the field of artificial intelligence. Proof planning, particularly in contexts requiring the validation of explanation accuracy, continu…