Calibrated Evidence Inference With a Small, Permissively Licensed Open Model
Systematic reviewers who use a model to read randomized trials need each answer and a measure of how far to trust it. If the model is calibrated, so that its stated probabilities match how often it is right, a reviewer can accept its confident answers and check the rest by hand. This paper fine-tunes Qwen3-4B-Base with low-rank adapters and a classification head for evidence inference (Lehman et al., 2019; DeYoung et al., 2020): given a trial's article and a prompt that names an intervention, a comparator and an outcome, classify the result as a significant increase, a significant decrease or no significant difference. The articles of Evidence Inference 2.0 come from the open-access subse...
| Authors | Johann Emmanuel Li |
|---|---|
| Primary category | cs.CL |
| Published | 4 October 2026 |
| Last updated at source | 15 October 2026 |
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