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LLM Annotators Show Social-Desirability Bias in Social Science Research

By PulseAugur Editorial · [1 sources] ·

A new paper investigates social-desirability bias in LLM annotators used for computational social science. Researchers found that three open-source models (Zephyr, Mistral-Instruct, and Qwen2.5-Instruct) exhibit different types of bias, such as leniency or overcorrection in labeling harmful content. The study also revealed that common prompting techniques do not effectively mitigate these biases and can sometimes exacerbate them, highlighting the need for more robust validation methods in CSS research. AI

RANK_REASON The cluster contains an academic paper detailing research findings on LLM bias. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

LLM Annotators Show Social-Desirability Bias in Social Science Research

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The cluster contains an academic paper detailing research findings on LLM bias. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Varun Kotte ·

    Two Wrongs, No Right: Auditing Social-Desirability Bias in LLM Annotators for Computational Social Science

    arXiv:2606.12426v1 Announce Type: cross Abstract: LLM annotators are increasingly used in computational social science (CSS), but it is unclear whether their alignment-shaped errors preserve the empirical conclusions a researcher would report. We audit three open-source 7B instru…

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