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arXiv Computation and Language By Maojun Sun, Yue Wu, Yifei Xie, Ruijian Han, Binyan Jiang, Defeng Sun, Yancheng Yuan, Jian Huang

DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval

Read the original on arXiv Computation and Language →

DARE introduces a distribution‑aware retrieval framework that enhances Large Language Model agents’ ability to select appropriate R functions for data science tasks. The system builds on a curated R function knowledge base (RPKB), a new retrieval method (DARE), an R‑oriented agent (RCodingAgent), and a benchmark (RStatBench). Experiments show significant gains in retrieval quality (up to 32.65 pp improvement in NDCG@10) and a near‑doubling of downstream analysis accuracy on RStatBench.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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