科学检查器再加载：一种用于透明性和逻辑推理的双向范式
Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical Reasoning
AI总结：
针对信息检索中的语义分歧、低精度和幻觉等问题，本文提出一种两模块双向方法，通过查询扩展和长文档内信息生成答案，提升透明性和逻辑推理能力。
摘要：
信息检索是一个快速发展的领域。然而，在面对科学和工业界海量信息时，它仍然面临显著的局限性，例如稀疏检索中的语义分歧和词汇差距、语义搜索中的低精度和缺乏可解释性，以及生成模型中的幻觉和过时信息。在本文中，我们提出了一种由两个模块组成的方法来应对长文档中的这些挑战。第一个模块通过查询扩展增强稀疏检索中的语言理解，以检索相关文档。第二个模块通过仅利用长文档中分布的信息，为复杂问题提供全面且信息丰富的答案，从而深化结果，实现双向参与。在流程的各个阶段，中间结果会呈现给用户，以促进对系统推理的理解。我们相信，这种双向方法在科学信息检索领域带来了透明度、逻辑思维和全面理解方面的显著进步。
英文摘要：
Information retrieval is a rapidly evolving field. However it still faces significant limitations in the scientific and industrial vast amounts of information, such as semantic divergence and vocabulary gaps in sparse retrieval, low precision and lack of interpretability in semantic search, or hallucination and outdated information in generative models. In this paper, we introduce a two-block approach to tackle these hurdles for long documents. The first block enhances language understanding in sparse retrieval by query expansion to retrieve relevant documents. The second block deepens the result by providing comprehensive and informative answers to the complex question using only the information spread in the long document, enabling bidirectional engagement. At various stages of the pipeline, intermediate results are presented to users to facilitate understanding of the system's reasoning. We believe this bidirectional approach brings significant advancements in terms of transparency, logical thinking, and comprehensive understanding in the field of scientific information retrieval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
