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Source note3 min read

Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks

Source

arXiv

arxiv.org/abs/2607.21482v1

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Source

Plain-Language Brief

Researchers have developed an open-source framework for evaluating the effectiveness of AI agents powered by large, locally deployable language models (LLMs) in data preparation tasks, specifically focused on longitudinal population studies. The framework includes a curated dataset, task definitions, and automated routines for evaluating the LLM-produced R code and outputted data. State-of-the-art 31-35B parameter models almost reached full performance in their benchmark tests, while open-weight LLMs running on consumer-grade hardware showed promise for AI-assisted data preparation in research settings with governance restrictions.

What the Document Says

  • The paper introduces an open-source framework for evaluating the efficacy of AI agents powered by open-weight large language models (LLMs) on data preparation tasks, specifically focused on longitudinal population studies.
  • The framework includes a curated ground-truth dataset, task definitions encompassing tasks like category harmonization and multi-wave merging, and automated routines for evaluating the LLM-produced R code and outputted data.
  • They benchmarked LLMs across different deployment spectra to assess their efficacy in 20 data preparation tasks (creation of 102 variables).
  • State-of-the-art, 31-35B parameter models almost saturated the benchmark with an 'average task completion' up to 87.9%.
  • The performance of open-weight LLMs running on consumer-grade hardware shows promise for a viable path toward AI-assisted data preparation in governance-restricted research settings.

Key Concepts Mentioned

  • large language models
  • agents
  • data preparation
  • longitudinal population studies
  • open-source framework
  • ground-truth dataset
  • task definitions
  • R code
  • consumer-grade hardware
  • governance restrictions

Claims Made by the Document

  • Their open-source framework is effective for evaluating the efficacy of AI agents powered by open-weight LLMs on data preparation tasks.
  • State-of-the-art, 31-35B parameter models perform well in their benchmark tests.
  • Open-weight LLMs running on consumer-grade hardware have potential for AI-assisted data preparation in governance-restricted research settings.

Evidence Used

  • The paper presents the introduction and details of the open-source framework.
  • Benchmark results are provided for different LLMs, including state-of-the-art models with 31-35B parameters.
  • The performance of open-weight LLMs running on consumer-grade hardware is discussed.

Methods / Approach

  • benchmarking large language models
  • development of an open-source framework for data preparation tasks

Limitations Stated by the Authors

  • Unknown if the authors have considered the potential biases or limitations in the data used for training their LLMs.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The agent may lack understanding of potential biases, errors, or limitations in the large language models being evaluated.
  • The agent does not evaluate the ethical implications of using AI-assisted data preparation in governance-restricted research settings.

Open Questions

  • What are the potential biases or limitations in the data used for training the large language models being evaluated?
  • What are the ethical implications of using AI-assisted data preparation in governance-restricted research settings?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper focuses on the technical aspects of evaluating AI agents powered by open-weight LLMs for data preparation tasks, without discussing potential biases, errors, or limitations in the models themselves.

Raw Abstract / Excerpt

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.