让大模型自己学会引用来源，减少幻觉并提升可信度。
Advancing Large Language Model Attribution through Self-Improving
- 用模型自动生成训练数据，先自我预热再逐步优化。
- 在三个问答数据集上平均提升25.13%，无需人工标注。
- 擅长整合多源信息，适合需要高可信回答的场景。
让大语言模型生成带引用来源的文本，可缓解幻觉问题，提升信息系统的可验证性。但提高该能力需高质量的引文数据，成本高且耗时。受自提升技术启发，我们提出START框架，通过迭代方式提升大模型的引文生成能力。首先，为避免初期监督信号不足导致停滞，模型自动生成合成训练数据用于预热。随后，利用采样响应构建细粒度偏好监督信号，持续引导模型生成更鲁棒、全面且可溯源的内容。在三个开放域问答数据集（涵盖长文本问答与多步推理）上的实验表明，无需人工标注和更高级模型，平均性能提升达25.13%。进一步分析显示，START在跨多个来源的信息聚合方面表现突出。
原文摘要 · Abstract (English)
Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems. However, improving this capability requires high-quality attribution data, which is costly and labor-intensive. Inspired by recent advances in self-improvement that enhance LLMs without manual annotation, we present START, a Self-Taught AttRibuTion framework for iteratively improving the attribution capability of LLMs. First, to prevent models from stagnating due to initially insufficient supervision signals, START leverages the model to self-construct synthetic training data for warming up. To further self-improve the model's attribution ability, START iteratively utilizes fine-grained preference supervision signals constructed from its sampled responses to encourage robust, comprehensive, and attributable generation. Experiments on three open-domain question-answering datasets, covering long-form QA and multi-step reasoning, demonstrate significant performance gains of 25.13% on average without relying on human annotations and more advanced models. Further analysis reveals that START excels in aggregating information across multiple sources.
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