Introduces a unified framework integrating uncertainty estimation, calibration, and tool-based abstention for reliable code predictions in language models.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.SE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes a two-stage on-the-fly input adaptation framework to reduce mispredictions in code language models across understanding tasks without retraining or additional supervision.
citing papers explorer
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When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions
Introduces a unified framework integrating uncertainty estimation, calibration, and tool-based abstention for reliable code predictions in language models.
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On-the-Fly Input Adaptation for Reliable Code Intelligence
Proposes a two-stage on-the-fly input adaptation framework to reduce mispredictions in code language models across understanding tasks without retraining or additional supervision.