Improving Reliability of Large Language Models via Claim-Level Self-Verification and Uncertainty Calibration

Catalog evidence and recorded chart appearances. Inclusion is not an endorsement.

Source snapshot imported 2026-10-20. This is not a live feed. Source and contributions.

Evidence and limits

Finding: The publisher record reports that decomposing answers into claims produced a useful uncertainty signal, while raw estimates still required post-hoc calibration.

Impact: Watch claim-level evidence and calibration as a candidate measurement approach for the recorded review topic (see source) and the recorded review topic (see source).

Boundary: One small benchmark, one answer and verifier model family, automated judging, and no human participants do not establish human trust, independent verification, or kit gains. Next: does the signal remain calibrated across models, datasets, evidence sources, and human review decisions? Candidate; not promoted or adopted

Flags: small single-benchmark evaluation; fixed calibration and held-out split; one model family; no human participants; closest calibrated baseline differences require caution; self-verification is not independent evidence

Record State: Candidate only: not accepted into the reviewed catalog

What was reviewed: Previously screened candidate; review remains limited

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Recorded chart appearances

  • 2026-10-13: position 68. Relevant to the recorded review topic (see source); Previously screened candidate; review remains limited evidence
  • 2026-10-12: position 67. Relevant to the recorded review topic (see source); Previously screened candidate; review remains limited evidence