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arXiv · 2601.04212

TrueBrief: Faithful Summarization through Small Language Models

Kumud Lakara·Ruibo Shi·Fran Silavong
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Abstract

Large language models (LLMs) have exhibited remarkable proficiency in generating high-quality text; however, their propensity for producing hallucinations poses a significant challenge for their deployment in security-critical domains. In this work, we present TrueBrief, an end-to-end framework specifically designed to enhance the faithfulness of small LLMs (SLMs) primarily for the task of text summarization through a preference-optimization paradigm. Central to our framework is a data generation module that facilitates controlled hallucination injection to generate synthetic preference data. Our work provides insights into the impact of data quality and model size on preference-based optimization, highlighting the conditions under which these methods are most effective.

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Kumud Lakara, Ruibo Shi, Fran Silavong. 2025-12-12. TrueBrief: Faithful Summarization through Small Language Models. https://arxiv.org/abs/2601.04212

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