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arXiv Machine Learning By Johann Emmanuel Li

Calibrated Evidence Inference With a Small, Permissively Licensed Open Model

Read the original on arXiv Machine Learning →

The paper presents a calibrated evidence inference model fine‑tuned from Qwen3‑4B‑Base using low‑rank adapters and a classification head to classify trial outcomes as significant increase, significant decrease, or no significant difference. Trained on 1,431 open‑access PubMed Central articles, the released model (Apache 2.0) achieves an expected calibration error of 0.017, a log loss lower than Gemma 4 31B‑it in zero‑shot settings, and a macro‑F1 of 0.925 on the test split. Accepting its top‑80 % most confident predictions yields only a 2.6 % error rate, and ablation studies show that article context influences answers while the title alone still provides useful signal.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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