Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy
  Searcher
Xiang Shi Jiawei Liu Yinpeng Liu Qikai Cheng Wei Lu
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.