5 papers
Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning
Liuji Chen, Dianxing Tang, Xing Shi +4
Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning. Existing approaches mitigate this issue w…
Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation
Liuji Chen, Xiaofang Yang, Yuanzhuo Lu +6
Retrieval-Augmented Generation (RAG) systems improve the factual grounding of large language models (LLMs) but remain vulnerable to retrieval poisoning, where adversaries seed the…
SEEM: Exploiting Black-Box Text Attacks to Manipulate Tool Selection
Liuji Chen, Hao Gao, Jinghao Zhang +3
Tool learning has emerged as a powerful auxiliary mechanism that extends the capabilities of large language models (LLMs), enabling them to address complex tasks that demand real-t…
From Profiles to Steering Vectors: Global Sparse Priors and Local Semantic Calibration for Personalized Text Generation
Liuji Chen, Zeyu Zhang, Xinyuan Zhang +4
Personalized text generation requires models to capture user-specific writing styles from historical data. Existing approaches based on retrieval, parameter-efficient fine-tuning,…
Graffe: Graph Representation Learning via Diffusion Probabilistic Models
Dingshuo Chen, Shuchen Xue, Liuji Chen +5
Diffusion probabilistic models (DPMs), widely recognized for their potential to generate high-quality samples, tend to go unnoticed in representation learning. While recent progres…