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Semantic Edit Regularization

techniqueactivesemantic-edit-regularization-24c35c07·1 events·first seen 26d ago

Aliases: Semantic Edit Regularization

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6arXiv · cs.CL·26d ago·source ↗

TextReg: Regularization Framework for Mitigating Prompt Distributional Overfitting in LLM Optimization

TextReg addresses a failure mode in iterative prompt optimization where LLM-rewritten prompts grow longer, accumulate narrow rules, and generalize poorly—termed prompt distributional overfitting. The authors formalize this via 'representational inefficiency,' a dual-factor measure decomposing prompt inefficiency into capacity cost and scope narrowness. TextReg applies a soft-penalty regularization framework using Dual-Evidence Gradient Purification, Semantic Edit Regularization, and Regularization-Guided Prompt Update. On reasoning benchmarks, it achieves up to +11.8% OOD accuracy over TextGrad and +16.5% over REVOLVE.