5 papers
Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
Huichi Zhou, Yihang Chen, Siyuan Guo +8
In this paper, we introduce a novel learning paradigm for Adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approach…
REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack
Zhonghao Zhan, Huichi Zhou, Hamed Haddadi
Graph Neural Network (GNN)-based network intrusion detection systems (NIDS) are often evaluated on single datasets, limiting their ability to generalize under distribution drift. F…
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis
Zhonghao Zhan, Huichi Zhou, Hamed Haddadi
Graph Neural Networks (GNNs) show great promise for Network Intrusion Detection Systems (NIDS), particularly in IoT environments, but suffer performance degradation due to distribu…
TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation
Huichi Zhou, Kin-Hei Lee, Zhonghao Zhan +5
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tai…
Verifiable Format Control for Large Language Model Generations
Zhaoyang Wang, Jinqi Jiang, Huichi Zhou +4
Recent Large Language Models (LLMs) have demonstrated satisfying general instruction following ability. However, small LLMs with about 7B parameters still struggle fine-grained for…