01 October 2026 | StageOne, Zurich
Knowing When Not to Answer: Bounding LLM Hallucinations in German Public Law
LLMs tend to guess even when the provided documents lack key facts. This is a major liability in high-stakes domains like German environmental and administrative law. In this talk, I will present our training framework based on the Merlin-Arthur protocol, which transforms LLM question answering into a two-player game. By training the model against both a helpful prover supplying key parts of the documents and an adversarial prover stripping away evidence, we force the model to ground its answers or explicitly abstain when evidence is missing. I will introduce our novel grounding score, which measures how much of an answer provably stems from the documents. I'll showcase how we applied this protocol to real German legal case files and considerably reduced hallucinations, shifting public sector AI from passive guesswork to verifiable reliability.
