Google researchers introduce 'faithful uncertainty,' allowing LLMs to offer best guesses instead of hallucinations

Google researchers propose “faithful uncertainty” to reduce LLM hallucinations by aligning hedged language with a model’s internal confidence, avoiding an answer-or-abstain utility tax. The approach reframes hallucinations as confident errors and helps agentic systems decide when to use external tools. They note challenges in training and evaluating true metacognition.