A logit-based method converts an LLM's numeric guesses into a calibrated prior over Pearson correlations and ranks expert-flagged hypotheses better than ranking by magnitude or by a fine-tuned RoBERTa classifier.
Data polygamy: The many-many relationships among urban spatio-temporal data sets
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Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior
A logit-based method converts an LLM's numeric guesses into a calibrated prior over Pearson correlations and ranks expert-flagged hypotheses better than ranking by magnitude or by a fine-tuned RoBERTa classifier.