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.
@article{arxiv.2601.04212,
title = {TrueBrief: Faithful Summarization through Small Language Models},
author = {Kumud Lakara and Ruibo Shi and Fran Silavong},
journal= {arXiv preprint arXiv:2601.04212},
year = {2026}
}