Towards Verifiable Text Generation with Evolving Memory and Self-Reflection

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.