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LLMs show demographic biases in text annotation, new study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper investigates the demographic biases inherent in large language models (LLMs) when they are used for text annotation. The study, conducted on the POPQUORN dataset, explores how LLMs mimic attributes like gender, race, and age, even when demographic information is not explicitly provided in the prompt. Researchers compared prompts with and without demographic conditioning, finding notable influences that contrast with some previous studies. AI

IMPACT Highlights potential biases in LLM-generated data, impacting the reliability and fairness of AI systems trained on such data.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM biases. [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 →

LLMs show demographic biases in text annotation, new study finds

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

  1. arXiv cs.CL TIER_1 English(EN) · Johannes Sch\"afer, Aidan Combs, Christopher Bagdon, Jiahui Li, Nadine Probol, Lynn Greschner, Sean Papay, Yarik Menchaca Resendiz, Aswathy Velutharambath, Amelie W\"uhrl, Sabine Weber, Roman Klinger ·

    Which Demographics do LLMs Default to During Annotation?

    arXiv:2410.08820v4 Announce Type: replace Abstract: Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find it offensive to read a message addressed to a "bro", but a male teenager might f…

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