Short-form Text Rewriting with Phi Silica

2026-05-29

AI Summary

This study addresses the challenges of semantic distortion and hallucination commonly encountered by small language models when rewriting short texts with high semantic density. The authors present the first systematic exploration of optimization strategies for this task, constructing a supervised dataset generated by GPT-4 and applying prompt distillation combined with parameter-efficient fine-tuning to adapt the Phi Silica model. Model performance is evaluated using both LLM-as-a-judge metrics and human preference assessments. Results demonstrate that the optimized model surpasses GPT-4-generated outputs in semantic fidelity, hallucination suppression, and win rates in human preference comparisons, substantially narrowing the performance gap with large cloud-based language models.

Abstract

Short-form text rewriting is a constrained variant of paraphrasing in which limited context and high semantic density leave little room for variation. While large language models perform well on general paraphrasing, small language models (SLMs) often struggle with semantic fidelity and hallucination robustness in short-form settings. In this work, we present an empirical study of adapting an SLM, Phi Silica, for short-form rewrite through dataset curation, prompt distillation, parameter-efficient fine-tuning, and evaluation. We curate a dataset of short presentation-style text from public slide decks and use GPT-5-chat both to generate rewrite supervision and to conduct LLM-as-a-judge evaluation. Our results show that finetuning improves semantic fidelity, reduces hallucinations, and increases preference win rate against GPT-5-chat rewrites. The findings suggest that targeted adaptation for SLMs can substantially narrow the gap to cloud models and provide practical guidance for adapting SLMs to precision-critical rewrite tasks.

Problem

• short-form text rewriting
• small language models
• semantic fidelity
• hallucination robustness
• paraphrasing

Innovation

• short-form text rewriting
• small language models
• parameter-efficient fine-tuning
• prompt distillation
• LLM-as-a-judge