用小模型生成更可信摘要，避免大模型幻觉问题。
TrueBrief: Faithful Summarization through Small Language Models
- 通过可控注入幻觉数据，生成偏好优化训练样本。
- 小模型经优化后摘要忠实度显著提升，接近大模型水平。
- 适合对可靠性要求高的摘要场景，如医疗、法律领域。
大型语言模型（LLMs）在生成高质量文本方面表现出色，但其容易产生幻觉，限制了在安全关键领域的应用。本文提出TrueBrief，一个端到端框架，旨在通过偏好优化范式提升小型语言模型（SLMs）在文本摘要任务中的忠实度。框架核心是一个数据生成模块，可控制地注入幻觉以生成合成偏好数据。研究揭示了数据质量与模型规模对基于偏好优化的影响，明确了此类方法最有效的条件。
原文摘要 · Abstract (English)
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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