Probing for Knowledge Attribution in Large Language Models 事件

Probing for Knowledge Attribution in Large Language Models arXiv:2602.22787v2 Announce Type: replace Abstract: Large language model (LLM) hallucinations, meaning fluent but factually incorrect generations, fall into two types: faithfulness violations, where the model misuses provided context, and factuality violations, where answers reflect errors in internal knowledge. Proper mitigation depends on knowing which source drives each answer. We study contributive attribution, i.e. the classificati