Change the Number, Change the Answer: Assessing Rating Scale Sensitivity in LLM-Based Text Scoring

arXiv:2610.22187v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in computational social science to assign scalar scores to social and psychological constructs. However, studies use different rating scales, and whether their scores remain comparable across scale designs is understudied. To address this gap, we define and systematically investigate scale sensitivity, the variation in LLM-generated scores caused by rating scale design. We distinguish numerical sensitivity, changes in normalized scores, from semantic sensitivity, changes in score interpretation

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