5 papers
Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks
Haowei Fu, Bo Ni, Han Xu +3
Retrieval-Augmented Generation (RAG) and Supervised Finetuning (SFT) have become the predominant paradigms for equipping Large Language Models (LLMs) with external knowledge for di…
SPAN: Benchmarking and Improving Cross-Calendar Temporal Reasoning of Large Language Models
Zhongjian Miao, Hao Fu, Chen Wei
We introduce SPAN, a cross-calendar temporal reasoning benchmark, which requires LLMs to perform intra-calendar temporal reasoning and inter-calendar temporal conversion. SPAN feat…
Towards Automatic Continual Learning: A Self-Adaptive Framework for Continual Instruction Tuning
Peiyi Lin, Fukai Zhang, Kai Niu +1
Continual instruction tuning enables large language models (LLMs) to learn incrementally while retaining past knowledge, whereas existing methods primarily focus on how to retain o…
Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents
Haochen Sun, Shuwen Zhang, Lujie Niu +6
Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. This paper proposes a new LLM-based Multi-Agent Sy…
Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach
Param Kulkarni, Yingchi Liu, Hao-Ming Fu +8
Achieving a delicate balance between fostering trust in law enforcement and protecting the rights of both officers and civilians continues to emerge as a pressing research and prod…