Towards Verifiable Text Generation with Evolving Memory and Self-Reflection
This addresses the critical issue of unreliable text generation in LLMs for applications requiring factual accuracy, though it appears incremental as it builds on existing verifiable generation approaches.
The paper tackles the problem of factual inaccuracies (hallucinations) in large language models by proposing VTG, a framework for verifiable text generation with citations, which significantly outperforms baselines on five datasets across three knowledge-intensive tasks.
Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also known as hallucination. A promising solution to this issue 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 intricate reasoning needed to align the claim with correct citations, and the dilemma between the precision and breadth of retrieved documents. In this paper, we present VTG, an innovative framework for Verifiable Text Generation with evolving memory and self-reflection. VTG introduces evolving long short-term memory to retain both valuable documents and recent documents. A two-tier verifier equipped with an evidence finder is proposed to rethink and reflect on the relationship between the claim and citations. Furthermore, active retrieval and diverse query generation are utilized to enhance both the precision and breadth of the retrieved documents. We conduct extensive experiments on five datasets across three knowledge-intensive tasks and the results reveal that VTG significantly outperforms baselines.