1 citations · 1 across the 7 of their papers we have counts for
8 papers
The "Knowledge-Behavior Gap" in Cultural Taboo Safety of Large Language Models
Ying He, Sihang Jiang, Xingzhou Chen +6
Cultural taboo safety is essential for deploying large language models (LLMs), as culturally insensitive outputs may cause offense or even social harm. However, existing cultural b…
Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents
Lujia Zhang, Xingzhou Chen, Hongwei Feng
Large language model (LLM) agents are increasingly used for complex information-extraction tasks, yet it remains unclear whether agentic components such as reflection and memory le…
Self-Evolving Deep Research via Joint Generation and Evaluation
Han Zhu, Chengkun Cai, Yuanfeng Song +3
Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capability. Unlike traditional ques…
SEIF: Self-Evolving Reinforcement Learning for Instruction Following
Qingyu Ren, Qianyu He, Jiajie Zhu +7
Instruction following is a fundamental capability of large language models (LLMs), yet continuously improving this capability remains challenging. Existing methods typically rely e…
From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks
Qingyu Ren, Tianjun Pan, Xingzhou Chen +1
Large language models have achieved remarkable progress in text generation but still struggle with generative writing tasks. In terms of evaluation, existing benchmarks evaluate wr…
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…
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…
StrucText-Eval: Evaluating Large Language Model's Reasoning Ability in Structure-Rich Text
Zhouhong Gu, Haoning Ye, Xingzhou Chen +3
The effective utilization of structured data, integral to corporate data strategies, has been challenged by the rise of large language models (LLMs) capable of processing unstructu…