Large Language Models (LLMs) face several challenges, including the tendency
to produce incorrect outputs, known as hallucination. An effective solution is
verifiable text generation, which prompts LLMs to generate content with
citations for accuracy verification. However, verifiable text generation is
non-trivial due to the focus-shifting phenomenon, the dilemma between the
precision and scope in document retrieval, and the intricate reasoning required
to discern the relationship between the claim and citations. In this paper, we
present VTG, an innovative approach for Verifiable Text Generation with
evolving memory and self-reflection. VTG maintains evolving long short-term
memory to retain both valuable documents and up-to-date documents. Active
retrieval and diverse query generation are utilized to enhance both the
precision and scope of the retrieved documents. Furthermore, VTG features a
two-tier verifier and an evidence finder, enabling rethinking and reflection on
the relationship between the claim and citations. We conduct extensive
experiments on five datasets across three knowledge-intensive tasks and the
results reveal that VTG significantly outperforms existing baselines.

该论文介绍了一种创新的可验证文本生成方法，利用演化记忆和自我反思进行文本生成。该方法通过保持演化的长短期记忆来保存有价值和最新的文档，利用主动检索和多样化查询生成来增强检索文档的精度和范围，并通过两层的验证器和证据查找器来反思和思考论点与引用之间的关系。在五个数据集上进行了广泛实验，结果表明该方法明显优于现有基准。