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arXiv AI By Jian Gao, Xiao Zhang, Xun Zhu, Miao Li, Ji Wu

Task-Level Natural Language Priors as Learning Signals for Low-Resource LLM Training

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Task-Level Natural Language Priors as Learning Signals for Low-Resource LLM Training proposes Prior-Guided Tuning (PGT), a training approach that treats natural-language priors as auxiliary learning signals rather than just input context. The method introduces Contrastive Prior Steering (CPS), which adds positive and negative prior-conditioned auxiliary losses while preserving the original supervised objective. Experiments on AmbiMath, Jigsaw, and MNLI/HANS demonstrate that CPS consistently outperforms plain and prompt fine-tuning, achieving high accuracy and significant gains with limited training data.

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 AI.

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