Factual Decoding Method Uses Internal Attribution Signals to Curb LLM Hallucination

A new arXiv preprint proposes a decoding strategy that draws on internal attribution signals within a large language model to keep generation factually grounded as it proceeds token by token. The authors argue that early factual mistakes tend to snowball during autoregressive generation, and that neither post-hoc fixes nor edits at the weight level reliably correct them. Their approach aims to intervene during decoding rather than after the fact.