484 citations · 515 across the 26 of their papers we have counts for
15 papers · 1 filter
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…
Large Emotional World Model
Changhao Song, Yazhou Zhang, Hui Gao +2
The world is governed by both physical laws and affective dynamics. Physical laws govern state transitions, while affective dynamics shape human actions, decisions, and interaction…
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…
Emotion-o1: Adaptive Long Reasoning for Emotion Understanding in LLMs
Changhao Song, Yazhou Zhang, Hui Gao +2
Long chain-of-thought (CoT) reasoning has shown great promise in enhancing the emotion understanding performance of large language models (LLMs). However, current fixed-length CoT…
NurValues: Real-World Nursing Values Evaluation for Large Language Models in Clinical Context
Ben Yao, Qiuchi Li, Yazhou Zhang +4
While LLMs have demonstrated medical knowledge and conversational ability, their deployment in clinical practice raises new risks: patients may place greater trust in LLM-generated…
Beyond Single-Sentence Prompts: Upgrading Value Alignment Benchmarks with Dialogues and Stories
Yazhou Zhang, Qimeng Liu, Qiuchi Li +2
Evaluating the value alignment of large language models (LLMs) has traditionally relied on single-sentence adversarial prompts, which directly probe models with ethically sensitive…
Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models
Yazhou Zhang, Chunwang Zou, Bo Wang +1
Sarcasm detection, as a crucial research direction in the field of Natural Language Processing (NLP), has attracted widespread attention. Traditional sarcasm detection tasks have t…
Edu-Values: Towards Evaluating the Chinese Education Values of Large Language Models
Peiyi Zhang, Yazhou Zhang, Bo Wang +3
In this paper, we present Edu-Values, the first Chinese education values evaluation benchmark that includes seven core values: professional philosophy, teachers' professional ethic…
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
Yazhou Zhang, Chunwang Zou, Zheng Lian +2
In the era of large language models (LLMs), the task of ``System I''~-~the fast, unconscious, and intuitive tasks, e.g., sentiment analysis, text classification, etc., have been ar…
Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?
Ben Yao, Yazhou Zhang, Qiuchi Li +1
Elaborating a series of intermediate reasoning steps significantly improves the ability of large language models (LLMs) to solve complex problems, as such steps would evoke LLMs to…
Chain of Stance: Stance Detection with Large Language Models
Junxia Ma, Changjiang Wang, Hanwen Xing +2
Stance detection is an active task in natural language processing (NLP) that aims to identify the author's stance towards a particular target within a text. Given the remarkable la…
Pushing The Limit of LLM Capacity for Text Classification
Yazhou Zhang, Mengyao Wang, Chenyu Ren +4
The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) acro…
DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations
Yazhou Zhang, Mengyao Wang, Youxi Wu +4
Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new visi…