让大模型更懂R统计工具，通过数据分布信息提升代码检索准确率。
DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval
- 基于数据分布特征融合函数元信息，构建R包检索嵌入模型。
- 在8,191个CRAN包上实现93.47%的NDCG@10，优于现有模型17%。
- 适合需要精准生成统计代码的研究者和自动化分析场景。
大型语言模型（LLM）代理可自动化数据科学流程，但许多成熟的统计方法因在R中实现而未被充分使用，原因是LLM难以获取统计知识并准确检索工具。现有增强检索方法仅关注函数语义，忽略数据分布，导致匹配效果不佳。本文提出DARE（Distribution-Aware Retrieval Embedding），一种轻量级、即插即用的检索模型，将数据分布信息融入函数表示以提升R包检索相关性。主要贡献包括：(i) RPKB，一个源自8,191个高质量CRAN包的结构化知识库；(ii) DARE，融合分布特征与函数元数据的嵌入模型，显著提升检索相关性；(iii) RCodingAgent，面向R的LLM代理，支持可靠代码生成及一系列统计分析任务，用于在真实分析场景下系统评估LLM代理性能。实验表明，DARE在包检索任务中达到93.47%的NDCG@10，相比最优开源嵌入模型提升高达17%，且参数量显著更少。将其集成至RCodingAgent后，在下游分析任务中取得显著提升。本工作有效缩小了大模型自动化与成熟R统计生态之间的差距。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents can automate data-science workflows, but many rigorous statistical methods implemented in R remain underused because LLMs struggle with statistical knowledge and tool retrieval. Existing retrieval-augmented approaches focus on function-level semantics and ignore data distribution, producing suboptimal matches. We propose DARE (Distribution-Aware Retrieval Embedding), a lightweight, plug-and-play retrieval model that incorporates data distribution information into function representations for R package retrieval. Our main contributions are: (i) RPKB, a curated R Package Knowledge Base derived from 8,191 high-quality CRAN packages; (ii) DARE, an embedding model that fuses distributional features with function metadata to improve retrieval relevance; and (iii) RCodingAgent, an R-oriented LLM agent for reliable R code generation and a suite of statistical analysis tasks for systematically evaluating LLM agents in realistic analytical scenarios. Empirically, DARE achieves an NDCG at 10 of 93.47%, outperforming state-of-the-art open-source embedding models by up to 17% on package retrieval while using substantially fewer parameters. Integrating DARE into RCodingAgent yields significant gains on downstream analysis tasks. This work helps narrow the gap between LLM automation and the mature R statistical ecosystem.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。