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.

利用大型语言模型（LLM）的知识构建了一个新的生成检索框架，以提高相关性和直接回答搜索问题的能力，验证生成结果的可靠性和贡献来源的可信度，实现了在 LLM 时代的 “PageRank”。该框架包括生成器、验证器和优化器三个核心模块，分别关注生成可信任的在线来源、验证来源可靠性以及优化不可靠的来源。通过广泛的实验和评估，证明了我们方法在相关性、负责性和可信性上对多种最先进方法的优越性。

明确前往方向：使 LLM 成为一个相关、负责任和可信赖的搜索引擎

Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy  Searcher

Multi-source transfer learning has been proven effective when within-target
labeled data is scarce. Previous work focuses primarily on exploiting domain
similarities and assumes that source domains are richly or at least comparably
labeled. While this strong assumption is never true in practice, this paper
relaxes it and addresses challenges related to sources with diverse labeling
volume and diverse reliability. The first challenge is combining domain
similarity and source reliability by proposing a new transfer learning method
that utilizes both source-target similarities and inter-source relationships.
The second challenge involves pool-based active learning where the oracle is
only available in source domains, resulting in an integrated active transfer
learning framework that incorporates distribution matching and uncertainty
sampling. Extensive experiments on synthetic and two real-world datasets
clearly demonstrate the superiority of our proposed methods over several
baselines including state-of-the-art transfer learning methods.

本文提出了一种新的多源传递学习方法，它结合了源目标相似性和不同可靠性源之间的关系，并提出了一个综合的主动传递学习框架，包括分布匹配和不确定性采样，在合成和真实数据集上进行了广泛的实验，表明其在多项基准测试中优于其他现有模型。