63 citations · 73 across the 14 of their papers we have counts for
14 papers
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning
Zhong Zhang, Yaxi Lu, Yikun Fu +22
The recent progress of large language model agents has opened new possibilities for automating tasks through graphical user interfaces (GUIs), especially in mobile environments whe…
ToLeaP: Rethinking Development of Tool Learning with Large Language Models
Haotian Chen, Zijun Song, Boye Niu +8
Tool learning, which enables large language models (LLMs) to utilize external tools effectively, has garnered increasing attention for its potential to revolutionize productivity a…
Synergizing Large Language Models and Theory for Human-like Causal Reasoning
Yanxi Zhang, Xin Cong, Zhong Zhang +3
Performing causal reasoning akin to humans is fundamental to achieving strong artificial intelligence. However, existing studies on causal reasoning and large language models (LLMs…
Learning to Generate Structured Output with Schema Reinforcement Learning
Yaxi Lu, Haolun Li, Xin Cong +6
This study investigates the structured generation capabilities of large language models (LLMs), focusing on producing valid JSON outputs against a given schema. Despite the widespr…
AgentRM: Enhancing Agent Generalization with Reward Modeling
Yu Xia, Jingru Fan, Weize Chen +7
Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning t…
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs
Runchu Tian, Yanghao Li, Yuepeng Fu +10
Positional bias in large language models (LLMs) hinders their ability to effectively process long inputs. A prominent example is the "lost in the middle" phenomenon, where LLMs str…
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Yaxi Lu, Shenzhi Yang, Cheng Qian +12
Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scena…
Learning Evolving Tools for Large Language Models
Guoxin Chen, Zhong Zhang, Xin Cong +5
Tool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature of…
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
Qinyu Luo, Yining Ye, Shihao Liang +10
Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging. However, their utilization in the d…
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Cheng Qian, Bingxiang He, Zhong Zhuang +8
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adep…
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Bohan Lyu, Xin Cong, Heyang Yu +9
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. Whi…
Seeing Is Not Always Believing: Invisible Collision Attack and Defence on Pre-Trained Models
Minghang Deng, Zhong Zhang, Junming Shao
Large-scale pre-trained models (PTMs) such as BERT and GPT have achieved great success in diverse fields. The typical paradigm is to pre-train a big deep learning model on large-sc…
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen +6
Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice…
Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models
Zhong Zhang, Bang Liu, Junming Shao
Pre-trained language models (PLMs) are known to be overly parameterized and have significant redundancy, indicating a small degree of freedom of the PLMs. Motivated by the observat…