5 papers
P2S: Probabilistic Process Supervision for General-Domain Reasoning Question Answering
Wenlin Zhong, Chengyuan Liu, Yiquan Wu +5
While reinforcement learning with verifiable rewards (RLVR) has advanced LLM reasoning in structured domains like mathematics and programming, its application to general-domain rea…
Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction
Yuting Huang, Chengyuan Liu, Yifeng Feng +4
As Large Language Models (LLMs) are widely applied in various domains, the safety of LLMs is increasingly attracting attention to avoid their powerful capabilities being misused. E…
EmotionHallucer: Evaluating Emotion Hallucinations in Multimodal Large Language Models
Bohao Xing, Xin Liu, Guoying Zhao +3
Emotion understanding is a critical yet challenging task. Recent advances in Multimodal Large Language Models (MLLMs) have significantly enhanced their capabilities in this area. H…
Towards Stepwise Domain Knowledge-Driven Reasoning Optimization and Reflection Improvement
Chengyuan Liu, Shihang Wang, Lizhi Qing +7
Recently, stepwise supervision on Chain of Thoughts (CoTs) presents an enhancement on the logical reasoning tasks such as coding and math, with the help of Monte Carlo Tree Search…
Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator
Chengyuan Liu, Shihang Wang, Lizhi Qing +4
Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs. But it still requires professional knowledge to facilitate the expertise for some…