9 papers
LH-Deception: Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions
Yang Xu, Xuanming Zhang, Samuel Yeh +4
Deception is a pervasive feature of human communication and an emerging concern in large language models (LLMs). While recent studies document instances of LLM deception, most eval…
LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signals
Samuel Yeh, Sharon Li, Tanwi Mallick
Retrieval-Augmented Generation (RAG) aims to mitigate hallucinations in large language models (LLMs) by grounding responses in retrieved documents. Yet, RAG-based LLMs still halluc…
Clean First, Align Later: Benchmarking Preference Data Cleaning for Reliable LLM Alignment
Samuel Yeh, Sharon Li
Human feedback plays a pivotal role in aligning large language models (LLMs) with human preferences. However, such feedback is often noisy or inconsistent, which can degrade the qu…
Cognition-of-Thought Elicits Social-Aligned Reasoning in Large Language Models
Xuanming Zhang, Yuxuan Chen, Samuel Yeh +1
Large language models (LLMs) excel at complex reasoning but can still exhibit harmful behaviors. Current alignment strategies typically embed safety into model weights, making thes…
MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
Xuanming Zhang, Yuxuan Chen, Samuel Yeh +1
Human social interactions depend on the ability to infer others' unspoken intentions, emotions, and beliefs-a cognitive skill grounded in the psychological concept of Theory of Min…
HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection
Min-Hsuan Yeh, Max Kamachee, Seongheon Park +1
To mitigate the impact of hallucination nature of LLMs, many studies propose detecting hallucinated generation through uncertainty estimation. However, these approaches predominant…