LL atrieval: LLM -Verified Retrieval for Verifiable Generation (2024)

Abstract: Verifiable generation aims to let the large language model (LLM) generate text with supporting documents, which enables the user to flexibly verify the answer and makes the LLM’s output more reliable. Retrieval plays a crucial role in verifiable generation. Specifically, the retrieved documents not only supplement knowledge to help the LLM generate correct answers, but also serve as supporting evidence for the user to verify the LLM’s output. However, the widely used retrievers become the bottleneck of the entire pipeline and limit the overall performance. Their capabilities are usually inferior to LLMs since they often have much fewer parameters than the large language model and have not been demonstrated to scale well to the size of LLMs. 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. To address these limitations, we propose **LLatrieval** (**L**arge **La**nguage Model Verified Re**trieval**),where the LLM updates the retrieval result until it verifies that the retrieved documents can sufficiently support answering the question. Thus, the LLM can iteratively provide feedback to retrieval and facilitate the retrieval result to fully support verifiable generation. Experiments on ALCE show that LLatrieval significantly outperforms extensive baselines and achieves state-of-the-art results.

## introduction large language models (llms) have shown remarkable abilities over various downstream tasks @xcite @xcite @xcite . however, llms struggle with factual errors, and often produce non-factual and fabricated content @xcite @xcite @xcite ; query docs retrieval answer (a) vanilla retrieval query docs retrieval support? yes no update answer (b) llatrieval figure 2: when the vanilla retrieval overshadows the llm's remarkable abilities in the pipeline, llatrieval can fully harness the llm's abilities to the retrieval by verify-update iterations. chern et al., 2023), which is usually referred to as "hallucination" and makes the llm's response not trustworthy. to address these challenges, researchers propose a new generation paradigm, verifiable generation @xcite @xcite , shown in figure 1 . for a given question, it requires the llm to generate the answer with corresponding supporting documents. in this way, these documents can serve as evidence and enable users to flexibly verify the answer, which makes the llm's response more reliable and facilitates its application in various important scenarios, medical diagnosis @xcite , scientific writing @xcite , situation reports @xcite , etc. retrieval plays a crucial role in verifiable generation. take retrieval-read, a typical pipeline for verifiable generation @xcite , as an example, shown in figure 2 (a). it is divided into two stages: 1) retrieve the relevant documents for the given question, 2) generate the answer according to the retrieved documents. on the one hand, the retrieved documents serve as knowledge supplement to help the llm generate the answer. on the other hand, the answer's verifiability relies on the retrieved documents. thus, both of the answer's correctness and verifiability highly rely on the quality of retrieval result. however, the widely used retrievers become the bottleneck of the entire pipeline and limit the overall performance @xcite . specifically, although the dense retrievers have achieved a series of state-of-the-art results in various scenarios @xcite @xcite , their capabilities are substantially inferior to llms @xcite , since they usually have much fewer parameters, and have not been demonstrated to scale well to the size of llms @xcite and achieve comparable capabilities. if the retriever does not correctly find the supporting documents, it is challenging for the llm to output the answer which is both correct and verifiable. in the typical pipeline, the llm usually receives the retrieval result in a passive manner and can not provide feedback to the low-quality retrieval even if it is capable of identifying that the retrieved documents are irrelevant to the question. these make the retrieval overshadow the llm's remarkable abilities and limit the overall performance. to address these limitations, we propose the framework of llatrieval (large language model verified retrieval), shown in figure 2(b) , where the llm fully provides feedback to the retrieval and augments it by the verify-update iteration: 1) retrieval verification: the llm verifies whether the retrieval result can support answering the give question. for questions whose documents fail the verification, we will update their documents and thus avoid low-quality retrieval. 2) retrieval update: the llm updates and improves the retrieval result. specifically, we propose progressive selection and missing-info query to let the llm progressively scan document candidates and supplement missing information to facilitate better verifiable generation. through the verify-update iteration, the llm can iteratively refine the retrieval result until it verifies that the retrieved documents can support answering the given question, and thus generate both correct and verifiable answers. we summarize our contributions as follows: ## background: verifiable generation in this paper, we follow the setting proposed by @xcite , which requires the llm to generate the answer with citations to the supporting documents, as shown in figure 1 . this setting is close to the real-world applications of generative search engines, e.g., new bing, and the citations allow the user to verify the answer more conveniently. we introduce its typical pipeline and the verifiability evaluation as follows. ## llatrieval: llm-verified retrieval in the retrieval-read pipeline, the llm passively receives the retrieval result and can not provide feedback to the low-quality retrieval, which makes the large language model with remarkable abilities overshadowed by the retriever with much fewer parameters. to address these limitations, we propose llatrieval, where the llm iteratively provides feedback to the retrieval through verify-update iterations, and thus fully harnesses its abilities for verifiable generation. in this section, we introduce the modules of retrieval verification and update, and the iterative verify-update process as follows. ## conclusion we propose llatrieval to retrieve supporting documents for verifiable generation, which lets the llm iteratively refine the retrieval result until the llm verifies the retrieved documents can support answering the given question. in this manner, llatrieval can fully harness the llm's ability and facilitate the retrieval result to help generate both correct and verifiable answers. experimental results show that our method outperforms extensive baselines and achieves new state-of-the-art results.