Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation (2025 · 19 citations)
Post-training turns base LLMs into instruction followers, but it needs high-quality instruction data. Such data is expensive to annotate, scarce, and often limited by privacy. Existing synthetic approaches such as Self-Instruct, WizardLM and Magpie usually produce instructions that are not tied to any realistic user…
MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools (2026 · 7 citations)
LLM agents increasingly need to work with tools exposed through the Model Contextual Protocol (MCP), but even strong models handle real MCP servers poorly.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review ↗arXiv:2412.01708 · no summary yetRui Ye et al.2024 · 35 citations
Synthesizing Post-Training Data for LLMs through Multi-Agent SimulationAnnual Meeting of the Association for Computational Linguistics (ACL)Shuo Tang, Xianghe Pang, Zexi Liu, et al.2025 · 19 citations
MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP ToolsAnnual Meeting of the Association for Computational Linguistics (ACL)WenHao Wang, Peizhi Niu, Zhao Xu, et al.2026 · 7 citations

Co-authors here

5 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 47 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated 1 November 2026