6 papers
Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation
Xuan Feng, Guihong Liu, Tianlong Gu +5
The paper introduces Expert-Guided Mutual Distillation (EGMD), a method that improves multimodal fake news detection across domains by calibrating input coherence, aligning domain…
Self-Debias: Self-correcting for Debiasing Large Language Models
Xuan Feng, Shuai Zhao, Luwei Xiao +2
Although Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, inherent social biases often cascade throughout the Chain-of-Thought (CoT) process, leading to…
Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shaping
Jianxiong Zhang, Bing Guo, Yuming Jiang +3
Large reasoning models (LRMs) often generate long, seemingly coherent reasoning traces yet still produce incorrect answers, making hallucination detection challenging. Although tra…
AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search
Qingyao Li, Weiwen Liu, Weinan Zhang +2
Recent advancements in Large Language Models (LLMs) have successfully employed search-based strategies to enhance code generation. However, existing methods typically rely on stati…
Conditional Performance Guarantee for Large Reasoning Models
Jianguo Huang, Hao Zeng, Bingyi Jing +2
Large reasoning models have shown strong performance through extended chain-of-thought reasoning, yet their computational cost remains significant. Probably approximately correct (…
On the Provable Performance Guarantee of Efficient Reasoning Models
Hao Zeng, Jianguo Huang, Bingyi Jing +2
Large reasoning models (LRMs) have achieved remarkable progress in complex problem-solving tasks. Despite this success, LRMs typically suffer from high computational costs during d…