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LLM demographic prompting shows bias, inconsistency

By PulseAugur Editorial · [2 sources] ·

New research indicates that using sociodemographic prompting with large language models (LLMs) to align their judgments with specific demographic groups can lead to unintended consequences. One study found that models without demographic conditioning tend to favor the judgments of majority groups, and that conditioning can disproportionately move predictions away from minority groups, especially in sensitive contexts. Another paper highlights that different demographic cues, such as names, can produce inconsistent results regarding LLM personalization and bias, suggesting that models respond to linguistic signals rather than stable demographic categories. Both studies call for caution and more nuanced evaluation methods when using demographic information with LLMs. AI

IMPACT Highlights potential biases and inconsistencies in LLM responses to demographic cues, urging caution in their application.

RANK_REASON Two academic papers published on arXiv discussing LLM behavior with demographic cues.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM demographic prompting shows bias, inconsistency

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Daniela Occhipinti, Andrea Piergentili, Marco Guerini ·

    LLM-as-a-Demographic: Whom Sociodemographic Prompting Helps, and Whom It Hurts

    arXiv:2609.00222v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how accurate a judge is, but whose judgments it reproduces. Sociodemographic promptin…

  2. arXiv cs.CL TIER_1 English(EN) · Manuel Tonneau, Neil K. R. Sehgal, Niyati Malhotra, Sharif Kazemi, Victor Orozco-Olvera, Ana Mar\'ia Mu\~noz Boudet, Lakshmi Subramanian, Samuel P. Fraiberger, Sharath Chandra Guntuku, Valentin Hofmann ·

    Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias

    arXiv:2601.18486v3 Announce Type: replace Abstract: Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes within and across groups. This approach typically relies on a single cue (e.g., n…

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