From token probabilities to calibrated confidence: An empirical study of mathematical question answering | SpinGraph: Technical nuance framing
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From token probabilities to calibrated confidence: An empirical study of mathematical question answering
confidence calibration, mathematical reasoning, LLM reliability, token probabilities, self-verification
Token probabilities—though individually overconfident—can yield informative confidence signals when aggregated across full answer sequences. Multi-pass methods (self-verification, Monte Carlo Dropout) achieve better calibration than single-pass baselines. Post-hoc calibration (Platt scaling, isotonic regression) reduces in-domain error but shows limited cross-dataset and cross-model transferability.
Emphasizes methodological variety and statistical improvement; minimizes practical deployment barriers, computational overhead, dataset specificity, and absence of real-world validation.
Rigorous, incremental, empirically grounded ML research advancing LLM trustworthiness through measurable calibration gains.
New research shows token probabilities can be calibrated for math QA using aggregation and multi-pass methods like self-verification and Monte Carlo Dropout.
Computational cost of multi-pass methods; Model size and architecture dependencies; Real-world latency impact; Failure modes on edge-case math problems
The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as well-calibrated, empirical accuracy, data efficiency, asymmetrically. The distribution reads as research distribution. A pressure point: Computational cost of multi-pass methods.
Aggregating token probabilities over the full sequence captures small but consistent differences between correct and incorrect generations, yielding more informative confidence estimates.
While individual token probabilities can be highly saturated, we find that aggregating token probabilities over the full sequence captures small but consistent differences between correct and incorrect generations, yielding more informative confidence estimates.
Extracted statistics from the source narrative
Self-verification and Monte Carlo Dropout