We are training a model on medical journals, but it keeps "inventing" citations that don't exist. Since this is a high-stakes domain, we need a 0% hallucination rate. Is fine-tuning enough, or do we need a specialized validation layer to cross-check every output?
3 answers
Fine-tuning is rarely the solution for factual accuracy; it's for tone and format. For 0% hallucinations in healthcare, you need a "Double-Check" architecture. First, use a RAG system to fetch real documents. Second, use a "Verifier Model" whose only job is to compare the generated answer against the fetched snippets. If the verifier finds any info in the answer that isn't in the source, it flags it for human review. We implemented this in late 2023 for a diagnostic tool, and it brought our factual error rate down to near zero by simply refusing to answer ungrounded questions.
Have you tried "Chain-of-Verification" (CoVe) prompting, where the model first generates a set of verification questions for itself before finalizing the medical response?
There is no such thing as a 0% hallucination rate with LLMs. You must have a human doctor in the loop to sign off on anything before it reaches a patient.
Martha is 100% right. AI should be an assistant, not a replacement. The "Human-in-the-loop" is the ultimate safety net for high-stakes industries.
I've read about CoVe, but I'm worried about the latency. In a clinical setting, doctors need answers in seconds. Does the CoVe process double the token cost and time, or is there a way to run those verification steps in parallel? Also, would using a specialized model like Med-PaLM 2 reduce the need for these complex verification layers from the start?