Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with in-line citations, have shown potential in mitigating hallucinations and improving verifiability. However, current approaches suffer from suboptimal citation quality due to their reliance on in-context learning. Furthermore, the practice of citing only coarse document identifiers makes it challenging for users to perform fine-grained verification. In this work, we introduce FRONT, a training framework designed to teach LLMs to generate Fine-Grained Grounded Citations. By grounding model outputs in fine-grained supporting quotes, these quotes guide the generation of grounded and consistent responses, not only improving citation quality but also facilitating fine-grained verification. Experiments on the ALCE benchmark demonstrate the efficacy of FRONT in generating superior grounded responses and highly supportive citations. With LLaMA-2-7B, the framework significantly outperforms all the baselines, achieving an average of 14.21% improvement in citation quality across all datasets, even surpassing ChatGPT.

翻译：尽管大语言模型在信息检索任务上表现出色，但仍受幻觉问题困扰。可归因大语言模型通过在内联文本中添加引文，在缓解幻觉和提高可验证性方面展现出潜力。然而，现有方法因依赖上下文学习而导致引文质量欠佳。此外，仅引用粗粒度文档标识符的做法使得用户难以进行细粒度验证。本研究提出FRONT训练框架，旨在教导大语言模型生成细粒度基础引文。通过将模型输出锚定在细粒度支持性引文片段上，这些引文片段引导生成具有基础支撑且逻辑一致的回答，不仅提升了引文质量，也促进了细粒度验证。在ALCE基准测试上的实验表明，FRONT在生成优质基础回答和高度支持性引文方面效果显著。使用LLaMA-2-7B模型时，该框架显著超越所有基线方法，在所有数据集上平均实现14.21%的引文质量提升，甚至优于ChatGPT。