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.
Riding tandem: Does cycling infrastructure investment mirror gentrification and privilege in portland, or and chicago, il? Research in Transportation Economics, 60:14–24, 2016
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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.