4 papers
HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering
Chaodong Tong, Qi Zhang, Zhuojun Jiang +2
Large language models (LLMs) achieve strong question answering (QA) performance but can produce fluent answers unsupported by available evidence. Existing hallucination detectors o…
Knowledge Dependency Estimation for Reliable Question Answering
Chaodong Tong, Qi Zhang, Nannan Sun +2
Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In realistic LLM-based QA, thi…
FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answering
Chaodong Tong, Qi Zhang, Chen Li +2
Faithfulness hallucinations in VQA occur when vision-language models produce fluent yet visually ungrounded answers, severely undermining their reliability in safety-critical appli…
Semantic Reformulation Entropy for Robust Hallucination Detection in QA Tasks
Chaodong Tong, Qi Zhang, Lei Jiang +3
Reliable question answering with large language models (LLMs) is challenged by hallucinations, fluent but factually incorrect outputs arising from epistemic uncertainty. Existing e…