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

Levering data on social media, such as Twitter and Facebook, requires
information retrieval algorithms to become able to relate very short text
fragments to each other. Traditional text similarity methods such as tf-idf
cosine-similarity, based on word overlap, mostly fail to produce good results
in this case, since word overlap is little or non-existent. Recently,
distributed word representations, or word embeddings, have been shown to
successfully allow words to match on the semantic level. In order to pair short
text fragments - as a concatenation of separate words - an adequate distributed
sentence representation is needed, in existing literature often obtained by
naively combining the individual word representations. We therefore
investigated several text representations as a combination of word embeddings
in the context of semantic pair matching. This paper investigates the
effectiveness of several such naive techniques, as well as traditional tf-idf
similarity, for fragments of different lengths. Our main contribution is a
first step towards a hybrid method that combines the strength of dense
distributed representations - as opposed to sparse term matching - with the
strength of tf-idf based methods to automatically reduce the impact of less
informative terms. Our new approach outperforms the existing techniques in a
toy experimental set-up, leading to the conclusion that the combination of word
embeddings and tf-idf information might lead to a better model for semantic
content within very short text fragments.

研究了基于单词嵌入和 tf-idf 相似性的方法，用于匹配短文本片段，并得出使用两种方法的混合方法可以更好地模拟非常短的文本片段中的语义内容。