Aleph Alpha released a method that teaches a language model to refuse answering when the supplied document lacks the needed information. The technique, called a Merlin‑Arthur protocol, pits the model (Arthur) against two helpers: Merlin, who supplies a complete passage, and Morgana, who removes the decisive sentence. During training Arthur never knows which side he faces, so he learns to answer only when the evidence is present and to abstain otherwise. Tests on five QA benchmarks show wrong answers drop by up to 35 percentage points and a new “grounding score” rises by as much as 0.38.
Why it matters
It gives users a reliable way to know when an AI’s answer truly comes from their own documents, reducing risky hallucinations.
