想知道开源模型做数据清洗行不行？这篇论文用真实研究数据测试了31-35B模型，近九成任务都能搞定，隐私保护场景也能用。
研究者提出开源框架RRBench，评估开源权重LLM驱动的AI代理在纵向人口研究数据准备中的效果。框架包括来自英国队列研究6轮数据的真实清洗脚本，以及20个数据准备任务（创建102个变量）。基准测试显示，31-35B参数模型平均任务完成度达87.9%，接近饱和。结果表明，消费级硬件上的开源模型为治理受限研究中的AI辅助数据准备提供了可行路径。
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
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.