4 papers
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…
LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls
Kangning Zhang, Wenxiang Jiao, Kounianhua Du +4
Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipel…
Evolve to Inspire: Novelty Search for Diverse Image Generation
Alex Inch, Passawis Chaiyapattanaporn, Yuchen Zhu +3
Text-to-image diffusion models, while proficient at generating high-fidelity images, often suffer from limited output diversity, hindering their application in exploratory and idea…
Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering
Kounianhua Du, Jianxing Liu, Kangning Zhang +6
The rapid evolution of large language models (LLMs) has intensified the demand for effective personalization techniques that can adapt model behavior to individual user preferences…