most citedLarge Language Models for Generative Information Extraction: A Survey

24 citations · 24 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems

Xufei Shi, Derong Xu, Wei Chen +3

Large language model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across diverse tasks. Yet their memory and collaboration architectures often remain s…

cs.AI2026

ATLAS: Dual-Horizon Diagnostic Evaluation for Tool-Use Agents in Meituan Xiaotua

Wei Chen, Peilun Zhou, Zhaoyu Hu +8

LLM-based assistants on web-scale service platforms increasingly address local-service needs through multi-step planning and tool use. Under dynamic business conditions and evolvin…

cs.CV2026

SAFE-G: Structure-aware Faithful Evidence-guided Generation for Knowledge-based Visual Question Answering

Long Shu, Shuochen Liu, Wei Chen +4

Knowledge-based Visual Question Answering (KB-VQA) aims to answer queries that necessitate reasoning over external knowledge sources beyond the visual content. Typically, current m…

cs.AI2026

DynaDebate: Breaking Homogeneity in Multi-Agent Debate with Dynamic Path Generation

Zhenghao Li, Zhi Zheng, Wei Chen +4

Recent years have witnessed the rapid development of Large Language Model-based Multi-Agent Systems (MAS), which excel at collaborative decision-making and complex problem-solving.…

cs.CL2023★ 24 cited

Large Language Models for Generative Information Extraction: A Survey

Derong Xu, Wei Chen, Wenjun Peng +7

Information extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable c…