Towards Label-Free Biological Reasoning Synthetic Dataset Creation via Uncertainty Filtering

• URL: http://arxiv.org/abs/2510.05871v1
• Date: Tue, 07 Oct 2025 12:40:37 GMT
• Title: Towards Label-Free Biological Reasoning Synthetic Dataset Creation via Uncertainty Filtering
• Authors: Josefa Lia Stoisser, Lawrence Phillips, Aditya Misra, Tom A. Lamb, Philip Torr, Marc Boubnovski Martell, Julien Fauqueur, Kaspar Märtens,
• Abstract summary: Synthetic chain-of-thought (CoT) traces are widely used to train large reasoning models (LRMs)<n>We propose a label-free alternative: uncertainty-based filtering, which uses a model's own confidence as a substitute for external labels.
• Score: 20.433272447607106
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthetic chain-of-thought (CoT) traces are widely used to train large reasoning models (LRMs), improving generalization by providing step-level supervision. Yet most approaches require ground-truth labels to seed or filter these traces - an expensive bottleneck in domains like biology where wet-lab data are scarce. We propose a label-free alternative: uncertainty-based filtering, which uses a model's own confidence - quantified through established uncertainty metrics like self-consistency and predictive perplexity - as a substitute for external labels. We sample multiple reasoning traces and retain only low-uncertainty subsets. Applied to biological perturbation prediction, a domain where wet-lab labels are especially costly, we show that the filtered subset has higher accuracy, and that supervised fine-tuning (SFT) on uncertainty-filtered data outperforms unfiltered synthetic data, narrows the gap to ground-truth training, and surpasses strong LRM baselines. Ablations show that per-class filtering corrects for class-specific uncertainty scales and that hybrid uncertainty metrics yield higher-quality datasets. Our results suggest that model-internal confidence is a powerful signal for efficient reasoning dataset creation, enabling LRMs in domains where supervision is expensive.

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