具有语义考虑的因果检索
Causal Retrieval with Semantic Consideration
AI总结：
CAWAI是一种结合语义和因果关系的检索模型，通过双重训练目标在因果检索任务中表现优异，尤其在大规模检索场景中具有良好的泛化能力。
摘要：
近年来，大型语言模型（LLMs）的进步显著提升了对话式人工智能系统的性能。为了将它们的能力扩展到需要知识密集型领域的应用，如生物医学和法律领域，其中准确性至关重要，LLMs经常与信息检索（IR）系统结合，以根据检索到的文档生成响应。然而，为了使IR系统能够有效支持此类应用，它们必须超越简单的语义匹配，并准确捕捉多样化的查询意图，包括因果关系。现有的IR模型主要侧重于基于表层语义相似性检索文档，忽视了如因果性等更深层次的关系结构。为了解决这个问题，我们提出了CAWAI，一个训练时具有语义和因果关系双重目标的检索模型。我们的广泛实验表明，CAWAI在各种因果检索任务上优于各种模型，尤其是在大规模检索设置下。我们还显示，CAWAI在科学领域问答任务中表现出强大的零样本泛化能力。
英文摘要：
Recent advancements in large language models (LLMs) have significantly enhanced the performance of conversational AI systems. To extend their capabilities to knowledge-intensive domains such as biomedical and legal fields, where the accuracy is critical, LLMs are often combined with information retrieval (IR) systems to generate responses based on retrieved documents. However, for IR systems to effectively support such applications, they must go beyond simple semantic matching and accurately capture diverse query intents, including causal relationships. Existing IR models primarily focus on retrieving documents based on surface-level semantic similarity, overlooking deeper relational structures such as causality. To address this, we propose CAWAI, a retrieval model that is trained with dual objectives: semantic and causal relations. Our extensive experiments demonstrate that CAWAI outperforms various models on diverse causal retrieval tasks especially under large-scale retrieval settings. We also show that CAWAI exhibits strong zero-shot generalization across scientific domain QA tasks.
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。
