TrueBrief: Faithful Summarization through Small Language Models

• URL: http://arxiv.org/abs/2601.04212v1
• Date: Fri, 12 Dec 2025 11:47:02 GMT
• Title: TrueBrief: Faithful Summarization through Small Language Models
• Abstract summary: Large language models (LLMs) have exhibited remarkable proficiency in generating high-quality text.<n>Their propensity for producing hallucinations poses a significant challenge for their deployment in security-critical domains.<n>We present TrueBrief, an end-to-end framework specifically designed to enhance the faithfulness of small LLMs.
• Score: 0.764671395172401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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