484 citations · 490 across the 11 of their papers we have counts for
17 papers
TextReasoningBench: Does Reasoning Really Improve Text Classification in Large Language Models?
Xinyu Guo, Yazhou Zhang, Jing Qin
Eliciting explicit, step-by-step reasoning traces from large language models (LLMs) has emerged as a dominant paradigm for enhancing model capabilities. Although such reasoning str…
Visual Room 2.0: Seeing is Not Understanding for MLLMs
Haokun Li, Yazhou Zhang, Jizhi Ding +2
Can multi-modal large language models (MLLMs) truly understand what they can see? Extending Searle's Chinese Room into the multi-modal domain, this paper proposes the Visual Room a…
Seeing is Not Understanding: A Benchmark on Perception-Cognition Disparities in Large Language Models
Haokun Li, Yazhou Zhang, Jizhi Ding +2
With the rapid advancement of Multimodal Large Language Models (MLLMs), they have demonstrated exceptional capabilities across a variety of vision-language tasks. However, current…
Large Language Models for Subjective Language Understanding: A Survey
Changhao Song, Yazhou Zhang, Hui Gao +2
Subjective language understanding refers to a broad set of natural language processing tasks where the goal is to interpret or generate content that conveys personal feelings, opin…
AffectGPT-R1: Leveraging Reinforcement Learning for Open-Vocabulary Multimodal Emotion Recognition
Zheng Lian, Fan Zhang, Yazhou Zhang +5
Open-Vocabulary Multimodal Emotion Recognition (OV-MER) aims to predict emotions without being constrained by label spaces, enabling fine-grained emotion understanding. Unlike trad…
MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination
Ao Jia, Haiming Wu, Guohui Yao +3
Large language models (LLMs) are prone to three types of hallucination: Input-Conflicting, Context-Conflicting and Fact-Conflicting hallucinations. The purpose of this study is to…