Although people are impressed by the content generation skills of large language models, the use of LLMs, such as ChatGPT, is limited by the domain grounding of the content. The correctness and groundedness of the generated content need to be based on a verified context, such as results from Retrieval-Augmented Generation (RAG). One important issue when adapting LLMs to a customized domain is that the generated responses are often incomplete, or the additions are not verified and may even be hallucinated. Prior studies on hallucination detection have focused on evaluation metrics, which are not easily adaptable to dynamic domains and can be vulnerable to attacks like jail-breaking. In this work, we propose 1) a post-processing algorithm that leverages knowledge triplets in RAG context to correct hallucinations and 2) a dual-decoder model that fuses RAG context to guide the generation process.

本研究解决了大型语言模型（LLMs）在特定领域生成内容时的正确性和可信度不足的问题。提出了一种后处理算法，利用检索增强生成（RAG）中的知识三元组来修正虚构内容，并引入了双解码器模型，通过融合RAG上下文来指导生成过程。该工作有望提高定制领域中LLMs生成内容的准确性和可靠性。

可信的大型语言模型：利用知识库和双解码器定制和落实文本生成