Task-Level Natural Language Priors as Learning Signals for Low-Resource LLM Training
Large language models (LLMs) often struggle when low-resource training data are ambiguous or incomplete. Task-level natural-language priors can provide useful guidance in such settings, but existing approaches usually treat these priors as input context rather than as learning signals during training. We study how task-level natural-language priors can supply guidance missing from these data through Prior-Guided Tuning (PGT). Its implementation, Contrastive Prior Steering (CPS), retains the supervised objective and adds positive- and negative-prior-conditioned losses with opposing optimization directions. Across AmbiMath, Diagnostic Anchoring, and Jigsaw, CPS achieves near-perfect exact-m...
| Authors | Jian Gao, Xiao Zhang, Xun Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 September 2026 |
| Last updated at source | 15 October 2026 |
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