今日 slides：research-radar-2026-07-17

每日文献雷达：2026-07-17

今日自动检索并筛选出 1 篇候选论文，通过结构化深度阅读生成以下分析。

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今日入选

• Leveraging LLMs to Enable Natural Language Search on Go-to-market Platforms（score: 0.185）

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Leveraging LLMs to Enable Natural Language Search on Go-to-market Platforms

• 作者：Jesse Yao, Saurav Acharya, Priyaranjan Parida, Srinivas Attipalli, Ali Dasdan
• 入选原因：appeared 2x before
• 来源信息：发表：arXiv.org | 链接：https://arxiv.org/abs/2411.05048

Enterprise searches require users to have complex knowledge of queries, configurations, and metadata, rendering it difficult for them to access information as needed.

• 摘要：Enterprise searches require users to have complex knowledge of queries, configurations, and metadata, rendering it difficult for them to access information as needed. Most go-to-market (GTM) platforms utilize advanced search, an interface that enables users to filter queries by various fields using categories or keywords, which, historically, however, has proven to be exceedingly cumbersome, as users are faced with seemingly hundreds of options, fields, and buttons. Consequently, querying with natural language has long been ideal, a notion further empowered by Large Language Models (LLMs).\ In this paper, we implement and evaluate a solution for the Zoominfo product for sellers, which prompts the LLM with natural language, producing search fields through entity extraction that are then con…

• 方法·三元组：当前环境未配置 LLM API Key，无法生成结构化三元组分析。GitHub Actions CI 中会使用百炼 API 进行深度阅读，采用公众号 storytelling 风格输出。本地可通过设置 LLM_API_KEY、LLM_BASE_URL、LLM_MODEL 环境变量启用。

• 实验：需确认数据集、指标和 baseline。

• 风险：离线或工具降级时摘要可能不足，不能替代人工精读。

• 后续动作：深读方法和实验设计，建议人工使用 /readpaper 精读

检索说明

• 检索层：arXiv（deepxiv）+ Semantic Scholar + Google Scholar，每日查询轮换，保证论文多样性。
• 阅读层：优先获取 arXiv HTML 全文，使用结构化三元组 + 公众号 storytelling 风格拆解论文逻辑。
• 深度阅读方法论参考 /readpaper 技能。
• 自动分析用于雷达筛选，重要论文仍需人工复核。