most citedChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CL2026

ExpertIVS: Sociological Expert Driven Individual Value Simulation in Large Language Models

Zhen Wang, Yuqi Ren, Yuehan Cui +7

Large Language Model (LLM) agents have demonstrated considerable potential for social simulation, yet struggle to accurately model individual value systems. Most existing methods m…

cs.CL20262 cited

ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents

Zhigen Li, Jianxiang Peng, Yanmeng Wang +13

Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of…

cs.CL2026

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan

Lei Yang, Leiyu Pan, Bojian Xiong +14

Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their performance remains heavily biased toward high-res…

cs.CL2026

DEP: A Decentralized Large Language Model Evaluation Protocol

Jianxiang Peng, Junhao Li, Hongxiang Wang +15

With the rapid development of Large Language Models (LLMs), a large number of benchmarks have been proposed. However, most benchmarks lack unified evaluation standard and require t…

cs.CL2025

ProBench: Benchmarking Large Language Models in Competitive Programming

Lei Yang, Renren Jin, Ling Shi +3

With reasoning language models such as OpenAI-o3 and DeepSeek-R1 emerging, large language models (LLMs) have entered a new phase of development. However, existing benchmarks for co…