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LLM scoring sensitivity to rating scales analyzed in new research paper

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the sensitivity of large language models (LLMs) to different rating scales when scoring text. The study, titled "Change the Number, Change the Answer: Assessing Rating Scale Sensitivity in LLM-Based Text Scoring," found that variations in scale design can significantly shift normalized scores and alter the categorization of texts. The research highlights that this sensitivity is particularly pronounced for toxicity assessments and that rubric-based prompting does not inherently reduce this variability compared to zero-shot methods. The authors offer guidance for more reliable LLM-based text scoring and emphasize the importance of robustness checks across various scales. AI

IMPACT Highlights potential unreliability in LLM-based scoring and provides guidance for more robust text analysis.

RANK_REASON Research paper published on arXiv detailing LLM sensitivity to rating scales. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM scoring sensitivity to rating scales analyzed in new research paper

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Research paper published on arXiv detailing LLM sensitivity to rating scales. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wonjeong Jo, Gyuwan Kim, Ming Shan Hee ·

    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 compa…

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