8 papers
AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions
Chen Chen, Xueluan Gong, Ziyao Liu +3
AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. With the rapid proliferation of AI and especially with the recent advancemen…
The Shadow Self: Intrinsic Value Misalignment in Large Language Model Agents
Chen Chen, Kim Young Il, Yuan Yang +7
Large language model (LLM) agents with extended autonomy unlock new capabilities, but also introduce heightened challenges for LLM safety. In particular, an LLM agent may pursue ob…
Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption
Huanyi Ye, Jiale Guo, Ziyao Liu +1
RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost. In traditional architectures, RAG services are provided by a single entit…
Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Survey
Yuqing Zhao, Ziyao Liu, Yongsen Zheng +1
Large Language Models (LLMs)-based question answering (QA) systems play a critical role in modern AI, demonstrating strong performance across various tasks. However, LLM-generated…
FROC: A Unified Framework with Risk-Optimized Control for Machine Unlearning in LLMs
Si Qi Goh, Yongsen Zheng, Ziyao Liu +2
Machine unlearning (MU) seeks to eliminate the influence of specific training examples from deployed models. As large language models (LLMs) become widely used, managing risks aris…
Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
Yongsen Zheng, Zongxuan Xie, Guohua Wang +3
Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age…