---
title: "无需云端：开源大模型在纵向数据准备任务中的评估框架"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.21482v1"
permalink: "https://feed.astralor.com/items/itm_d140dea79f32e7f8f5d490c4"
published: "2026-07-23T16:23:42.000Z"
score: 81
channel: "ai"
tags: ["RRBench","开源大模型","数据准备","纵向研究","隐私治理"]
---

# 无需云端：开源大模型在纵向数据准备任务中的评估框架

## AI 点评

> 这篇论文为数据治理严格的纵向研究领域提供了一个宝贵的基准，让我看到了开源模型在本地化数据准备中的巨大潜力。如果你是从事人口健康、社会科学等纵向数据研究的研究员或工程人员，这篇文章值得一读——它证明了消费级硬件上的开源模型已能胜任大多数数据清洗任务，大大降低了隐私合规风险下的AI应用门槛。

## 速览

本文提出一个开源框架，用于评估开源权重大语言模型在纵向人口研究数据准备任务中的效果。框架包含真实数据集、任务定义和自动评估流程，对20项数据准备任务（创建102个变量）进行基准测试。当前最先进的31-35B参数模型平均任务完成率达87.9%，证明在消费级硬件上运行的开源模型有望在治理受限的研究环境中实现AI辅助数据准备。

## 关键点

- 提出面向纵向数据准备任务的开源评估框架，涵盖六波数据清洗脚本、任务定义与自动评估
- 基准测试显示31-35B参数开源模型平均任务完成率达87.9%，接近饱和
- 证明在消费级硬件上运行的开源模型可作为云端模型替代方案，解决数据隐私与治理约束问题

## 全文

Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models. Their adoption in research using personal data is constrained by governance requirements that typically prohibit data transmission to external services. Locally deployable open-weight models offer an alternative since sensitive data never leave the local environment. We introduce an open-source framework for evaluating the efficacy of AI agents powered by open-weight LLMs on one of the most persistent bottlenecks in research on longitudinal population studies: data preparation. The framework comprises: a curated ground-truth dataset (cleaning scripts preparing six sweeps of data from a British cohort study), task definitions encompassing tasks such as category harmonization and multi-wave merging, and automated routines for evaluating the LLM-produced R code and outputted data. We benchmark LLMs across the (consumer grade) deployment spectrum to assess their efficacy in 20 data preparation tasks (creation of 102 variables). Current state-of-the-art, 31-35B parameter models almost saturated our benchmark ("average task completion" up to 87.9%). The performance of open-weight LLMs running on consumer-grade hardware shows promise of a viable path toward AI-assisted data preparation in governance-restricted research settings. Our framework is publicly available at: https://github.com/UCL-ARC/RRBench.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_d140dea79f32e7f8f5d490c4