most citedAgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2025

CultureScope: A Dimensional Lens for Probing Cultural Understanding in LLMs

Jinghao Zhang, Sihang Jiang, Shiwei Guo +7

As large language models (LLMs) are increasingly deployed in diverse cultural environments, evaluating their cultural understanding capability has become essential for ensuring tru…

cs.CL20251 cited

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

Zhouhong Gu, Xiaoxuan Zhu, Yin Cai +12

Large language model based multi-agent systems have demonstrated significant potential in social simulation and complex task resolution domains. However, current frameworks face cr…

cs.CL2025

ToReMi: Topic-Aware Data Reweighting for Dynamic Pre-Training Data Selection

Xiaoxuan Zhu, Zhouhong Gu, Baiqian Wu +5

Pre-training large language models (LLMs) necessitates enormous diverse textual corpora, making effective data selection a key challenge for balancing computational resources and m…

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.CL2025

GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization

Zhouhong Gu, Xingzhou Chen, Xiaoran Shi +5

Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to…