RAISE：通过逐步检索增强大语言模型的科学推理
RAISE: Enhancing Scientific Reasoning in LLMs via Step-by-Step Retrieval
AI总结：
RAISE通过逐步检索增强科学推理，其核心方法是问题分解、逻辑查询生成和逻辑检索，优于其他基线模型。
摘要：
科学推理不仅需要长链推理过程，还需要领域术语知识和适应更新发现。为应对这些挑战，我们引入RAISE，一种逐步检索增强框架，从真实语料库中检索逻辑相关的文档。RAISE分为三个步骤：问题分解、逻辑查询生成和逻辑检索。我们观察到RAISE在科学推理基准上 consistently 超过其他基线。我们分析发现，与其它基线不同，RAISE检索的文档不仅在领域知识上相似，而且在逻辑上更加相关。
英文摘要：
Scientific reasoning requires not only long-chain reasoning processes, but also knowledge of domain-specific terminologies and adaptation to updated findings. To deal with these challenges for scientific reasoning, we introduce RAISE, a step-by-step retrieval-augmented framework which retrieves logically relevant documents from in-the-wild corpus. RAISE is divided into three steps: problem decomposition, logical query generation, and logical retrieval. We observe that RAISE consistently outperforms other baselines on scientific reasoning benchmarks. We analyze that unlike other baselines, RAISE retrieves documents that are not only similar in terms of the domain knowledge, but also documents logically more relevant.
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。
