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English(EN) UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval

UnIte方法通过不确定性采样改进信息检索域自适应

研究人员开发了一种名为UnIte的新方法,用于信息检索中的无监督域自适应。该技术通过策略性地选择文档进行伪查询生成,提高了神经检索器在新领域的泛化能力。UnIte侧重于模型不确定性,过滤掉具有高随机不确定性的文档,并优先处理具有高认知不确定性的文档,以最大限度地提高学习效率。实验表明,在nDCG@10得分方面有了显著提高,尤其是在训练样本量较小的情况下。 AI

影响 引入了一种改进神经检索器泛化能力的新方法,有望提高在专业信息检索任务中的性能。

排序理由 这是一篇详细介绍信息检索中域自适应新方法的学术论文。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

UnIte方法通过不确定性采样改进信息检索域自适应

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这是一篇详细介绍信息检索中域自适应新方法的学术论文。
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    UnIte:信息检索领域自适应中的基于不确定性的迭代文档采样

    Unsupervised domain adaptation generalizes neural retrievers to an unseen domain by generating pseudo queries on target domain documents. The quality and efficiency of this adaptation critically depend on which documents are selected for pseudo query generation. The existing docu…