Verifiable generation aims to let the large language model (LLM) generate
text with corresponding supporting documents, which enables the user to
flexibly verify the answer and makes it more trustworthy. Its evaluation not
only measures the correctness of the answer, but also the answer's
verifiability, i.e., how well the answer is supported by the corresponding
documents. In typical, verifiable generation adopts the retrieval-read
pipeline, which is divided into two stages: 1) retrieve relevant documents of
the question. 2) according to the documents, generate the corresponding answer.
Since the retrieved documents can supplement knowledge for the LLM to generate
the answer and serve as evidence, the retrieval stage is essential for the
correctness and verifiability of the answer. However, the widely used
retrievers become the bottleneck of the entire pipeline and limit the overall
performance. They often have fewer parameters than the large language model and
have not been proven to scale well to the size of LLMs. Since the LLM passively
receives the retrieval result, if the retriever does not correctly find the
supporting documents, the LLM can not generate the correct and verifiable
answer, which overshadows the LLM's remarkable abilities. In this paper, we
propose LLatrieval (Large Language Model Verified Retrieval), where the LLM
updates the retrieval result until it verifies that the retrieved documents can
support answering the question. Thus, the LLM can iteratively provide feedback
to retrieval and facilitate the retrieval result to sufficiently support
verifiable generation. Experimental results show that our method significantly
outperforms extensive baselines and achieves new state-of-the-art results.

本研究提出了 LLatrieval（Large Language Model Verified Retrieval），在此模型中，大型语言模型通过更新检索结果来验证检索到的文档是否能够支持回答问题，从而使语言模型能够通过迭代地向检索提供反馈，以确保检索结果能够充分支持可验证的生成。实验结果表明，我们的方法明显优于广泛的基准，并取得了最新的研究成果。