5 papers
Don't Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models
Jinzhe Li, Gengxu Li, Yi Chang +1
Large language models (LLMs) have witnessed rapid advancements, demonstrating remarkable capabilities. However, a notable vulnerability persists: LLMs often uncritically accept fla…
Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability
Haiqi Yang, Jinzhe Li, Gengxu Li +2
Large Multimodal Models (LMMs) have witnessed remarkable growth, showcasing formidable capabilities in handling intricate multimodal tasks with exceptional performance. Recent rese…
Refining Critical Thinking in LLM Code Generation: A Faulty Premise-based Evaluation Framework
Jialin Li, Jinzhe Li, Gengxu Li +2
With the advancement of code generation capabilities in large language models (LLMs), their reliance on input premises has intensified. When users provide inputs containing faulty…
Length-Controlled Margin-Based Preference Optimization without Reference Model
Gengxu Li, Tingyu Xia, Yi Chang +1
Direct Preference Optimization (DPO) is a widely adopted offline algorithm for preference-based reinforcement learning from human feedback (RLHF), designed to improve training simp…
Asymmetric Co-Training for Source-Free Few-Shot Domain Adaptation
Gengxu Li, Yuan Wu
Source-free unsupervised domain adaptation (SFUDA) has gained significant attention as an alternative to traditional unsupervised domain adaptation (UDA), which relies on the const…