2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
Pingzhi Li, Morris Yu-Chao Huang, Zhen Tan +6
Knowledge Distillation (KD) accelerates training of large language models (LLMs) but poses intellectual property protection and LLM diversity risks. Existing KD detection methods b…
cs.CL2025★ 2 cited
MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models
Shrey Pandit, Jiawei Xu, Junyuan Hong +4
Advancements in Large Language Models (LLMs) and their increasing use in medical question-answering necessitate rigorous evaluation of their reliability. A critical challenge lies…
cs.CL2025
GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing
Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang +12
Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LL…