Selecting Language Models for Social Science: Start Small, Start Open, and Validate

• URL: http://arxiv.org/abs/2601.10926v1
• Date: Fri, 16 Jan 2026 01:01:47 GMT
• Title: Selecting Language Models for Social Science: Start Small, Start Open, and Validate
• Authors: Dustin S. Stoltz, Marshall A. Taylor, Sanuj Kumar,
• Abstract summary: We argue that social scientists cannot altogether avoid validating computational measures (ex-post)<n>Being able to reliably replicate a particular finding that entails the use of a language model requires reliably reproducing a task.<n>We propose starting with smaller, open models, and constructing benchmarks to demonstrate the validity of the entire computational pipeline.
• Score: 0.3823356975862005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Currently, there are thousands of large pretrained language models (LLMs) available to social scientists. How do we select among them? Using validity, reliability, reproducibility, and replicability as guides, we explore the significance of: (1) model openness, (2) model footprint, (3) training data, and (4) model architectures and fine-tuning. While ex-ante tests of validity (i.e., benchmarks) are often privileged in these discussions, we argue that social scientists cannot altogether avoid validating computational measures (ex-post). Replicability, in particular, is a more pressing guide for selecting language models. Being able to reliably replicate a particular finding that entails the use of a language model necessitates reliably reproducing a task. To this end, we propose starting with smaller, open models, and constructing delimited benchmarks to demonstrate the validity of the entire computational pipeline.

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