most citedAgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2025

LITE: LLM-Impelled efficient Taxonomy Evaluation

Lin Zhang, Zhouhong Gu, Suhang Zheng +4

This paper presents LITE, an LLM-based evaluation method designed for efficient and flexible assessment of taxonomy quality. To address challenges in large-scale taxonomy evaluatio…

cs.CL2025

RECKON: Large-scale Reference-based Efficient Knowledge Evaluation for Large Language Model

Lin Zhang, Zhouhong Gu, Xiaoran Shi +2

As large language models (LLMs) advance, efficient knowledge evaluation becomes crucial to verifying their capabilities. Traditional methods, relying on benchmarks, face limitation…

cs.CL2024

DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?

Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu +8

Detecting evidence within the context is a key step in the process of reasoning task. Evaluating and enhancing the capabilities of LLMs in evidence detection will strengthen contex…

cs.AI2024★ 1 cited

AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior

Zhouhong Gu, Xiaoxuan Zhu, Haoran Guo +10

Language significantly influences the formation and evolution of Human emergent behavior, which is crucial in understanding collective intelligence within human societies. Consider…

cs.CL2024

Efficiently Quantifying and Mitigating Ripple Effects in Model Editing

Jianchen Wang, Zhouhong Gu, Xiaoxuan Zhu +5

Large Language Models have revolutionized numerous tasks with their remarkable efficacy. However, editing these models, crucial for rectifying outdated or erroneous information, of…