An unsupervised technique extracts latent yes-no knowledge from language model activations by locating a direction that satisfies logical consistency properties, outperforming zero-shot accuracy by 4% on average across models and datasets.
Truthful ai: Developing and governing ai that does not lie
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GPT-3 can learn to express well-calibrated uncertainty about its answers using natural language phrases rather than logits.
The DECK taxonomy partitions LLM hallucinations into four detectability regimes using consistency and confidence axes, mapping each to scorer families and identifying a universal blind spot for output-level uncertainty quantification on knowledge-gap inputs.
Mini-Mafia supplies an analytical model logit(p) = v*(m-d) for mafia win probability in LLM role interactions and uses Bayesian inference to estimate per-model parameters that predict tournament results with 76.6% Brier-score improvement over random.
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
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