7 papers
OCR-MetaReasoning Benchmark: Evaluating the Meta-Reasoning Ability of MLLMs in Text-Rich Image Understanding
Gengxu Li, Yuan Wu, Yi Chang
Text-rich image understanding requires multimodal large language models (MLLMs) to organize OCR (Optical Character Recognition)-grounded evidence across words, layout, fields, char…
MMPCBench: Benchmarking Multimodal Large Language Models on Proactive Critique of Flawed Inputs
Jinzhe Li, Gengxu Li, Jinnan Li +2
As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructions but also on validating th…
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…
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…
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…
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…