7 papers
SRBench: A Comprehensive Benchmark for Sequential Recommendation with Large Language Models
Jianhong Li, Zeheng Qian, Wangze Ni +4
LLM development has aroused great interest in Sequential Recommendation (SR) applications. However, comprehensive evaluation of SR models remains lacking due to the limitations of…
Black-Box Guardrail Reverse-engineering Attack
Hongwei Yao, Yun Xia, Shuo Shao +3
Large language models (LLMs) increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful…
Quantifying Conversation Drift in MCP via Latent Polytope
Haoran Shi, Hongwei Yao, Shuo Shao +4
The Model Context Protocol (MCP) enhances large language models (LLMs) by integrating external tools, enabling dynamic aggregation of real-time data to improve task execution. Howe…
SoK: Large Language Model Copyright Auditing via Fingerprinting
Shuo Shao, Yiming Li, Yu He +4
The broad capabilities and substantial resources required to train Large Language Models (LLMs) make them valuable intellectual property, yet they remain vulnerable to copyright in…
BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF
Kaiwen Duan, Hongwei Yao, Yufei Chen +4
Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning text-to-image (T2I) models with human preferences. However, RLHF's feedback mechanism also opens new pathw…
ControlNET: A Firewall for RAG-based LLM System
Hongwei Yao, Haoran Shi, Yidou Chen +3
Retrieval-Augmented Generation (RAG) has significantly enhanced the factual accuracy and domain adaptability of Large Language Models (LLMs). This advancement has enabled their wid…