The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reliability of generated results and the credibility of contributing sources, due to the limitations of traditional information retrieval algorithms and the LLM hallucination problem. Aiming to create a "PageRank" for the LLM era, we strive to transform LLM into a relevant, responsible, and trustworthy searcher. We propose a novel generative retrieval framework leveraging the knowledge of LLMs to foster a direct link between queries and online sources. This framework consists of three core modules: Generator, Validator, and Optimizer, each focusing on generating trustworthy online sources, verifying source reliability, and refining unreliable sources, respectively. Extensive experiments and evaluations highlight our method's superior relevance, responsibility, and trustfulness against various SOTA methods.

翻译：大语言模型的出现展现了其在网络搜索中提升相关性和提供直接答案的潜力。然而，由于传统信息检索算法的局限性以及大语言模型的幻觉问题，验证生成结果的可靠性和贡献来源的可信度面临挑战。旨在创造大语言模型时代的"PageRank"，我们致力于将大语言模型转变为相关、负责且可信的搜索者。我们提出了一种新型生成式检索框架，利用大语言模型的知识建立查询与在线来源之间的直接关联。该框架包含三个核心模块：生成器、验证器和优化器，分别专注于生成可信的在线来源、验证来源可靠性以及优化不可靠来源。大量实验和评估表明，我们的方法在相关性、责任性和可信度方面优于各类现有最优方法。