Scaling Open-ended Survey Responses Using LLM-Paired Comparisons

Abstract

Survey researchers rely heavily on closed-ended questions to measure latent respondent characteristics like knowledge, policy positions, emotions, ideology, and various other traits. While closed-ended questions ease analysis and data collection, they necessarily limit the depth and variability of responses. Open-ended responses allow for greater depth and variability in responses but are labor-intensive to code. Large Language Models (LLMs) can solve some of these problems, but existing approaches to using LLMs have a number of limitations. In this paper, we propose and test a pairwise comparison method to scale open-ended survey responses on a continuous scale. The approach relies on LLMs to make pairwise comparisons of statements that identify which statement ``wins'' and ``loses''. With this information, we employ a Bayesian Bradley-Terry model to recover a `score' on a the relevant latent dimension for each statement. This approach allows for finer discrimination between items, better measures of uncertainty, reduces anchoring bias, and is more flexible than methods relying on Maximum Likelihood Estimation techniques. We demonstrate the utility of this approach on an open-ended question probing knowledge of interest rates in the US economy. A comparison of 6 LLMs of various sizes reveals that pairwise comparisons show greater consistency than zero-shot 0-10 ratings with larger models (> 9-billion parameters). Further, comparison of pairwise decisions are consistent with high-knowledge crowd source workers.

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