11 citations · 29 across the 14 of their papers we have counts for
15 papers
Is the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning
Yongyi Huang, Huaping Zhang, Wenyao Cui +3
Large language models (LLMs) remain unreliable when reasoning about the future. Existing models often overlook the difference among future states, committing to specific answers ev…
SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification
Wenyao Cui, Huaping Zhang, Yongyi Huang +6
Large language models (LLMs) increasingly serve as data-driven reasoners, yet their chains-of-thought (CoT) can be unfaithful even when final answers are correct. Most existing ``v…
Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
Yunhan Bu, Quan Zhang, Huaping Zhang +9
Multi-Hop Fact Verification requires complex reasoning across disparate evidence, posing significant challenges for Large Language Models , which may suffer from hallucinations and…
Improving Multimodal Reasoning via Worst Dimension Optimization
Haocheng Lv, Huaping Zhang, Qiuchi Li +2
Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models…
BadgeX: IoT-Enhanced Wearable Analytics Meets LLMs for Collaborative Learning
Zaibei Li, Shunpei Yamaguchi, Qiuchi Li +1
We present BadgeX, a novel system integrating lightweight wearable IoT devices (smart badges/smartphones) with Large Language Models (LLMs) to enable real-time collaborative learni…
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…
Are MLMs Trapped in the Visual Room?
Yazhou Zhang, Chunwang Zou, Qimeng Liu +6
Can multi-modal large models (MLMs) that can ``see'' an image be said to ``understand'' it? Drawing inspiration from Searle's Chinese Room, we propose the \textbf{Visual Room} argu…
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…
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…
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…
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Yaochen Liu, Qiuchi Li, Benyou Wang +2
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition…
Adapting Pre-trained Language Models for Quantum Natural Language Processing
Qiuchi Li, Benyou Wang, Yudong Zhu +2
The emerging classical-quantum transfer learning paradigm has brought a decent performance to quantum computational models in many tasks, such as computer vision, by enabling a com…