ReSS uses decision-tree scaffolds to fine-tune LLMs for faithful tabular reasoning, reporting up to 10% gains over baselines on medical and financial data.
LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks , publisher =
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Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
citing papers explorer
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ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold
ReSS uses decision-tree scaffolds to fine-tune LLMs for faithful tabular reasoning, reporting up to 10% gains over baselines on medical and financial data.
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Language Models (Mostly) Know What They Know
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.