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        <title>“Global is Good, Local is Bad?”: Understanding Brand Bias in LLMs</title>
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        <namePart type="given">Mahammed</namePart>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Many recent studies have investigated social biases in LLMs but brand bias has received little attention. This research examines the biases exhibited by LLMs towards different brands, a significant concern given the widespread use of LLMs in affected use cases such as product recommendation and market analysis. Biased models may perpetuate societal inequalities, unfairly favoring established global brands while marginalizing local ones. Using a curated dataset across four brand categories, we probe the behavior of LLMs in this space. We find a consistent pattern of bias in this space—both in terms of disproportionately associating global brands with positive attributes and disproportionately recommending luxury gifts for individuals in high-income countries. We also find LLMs are subject to country-of-origin effects which may boost local brand preference in LLM outputs in specific contexts.</abstract>
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%0 Conference Proceedings
%T “Global is Good, Local is Bad?”: Understanding Brand Bias in LLMs
%A Kamruzzaman, Mahammed
%A Nguyen, Hieu
%A Kim, Gene
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kamruzzaman-etal-2024-global
%X Many recent studies have investigated social biases in LLMs but brand bias has received little attention. This research examines the biases exhibited by LLMs towards different brands, a significant concern given the widespread use of LLMs in affected use cases such as product recommendation and market analysis. Biased models may perpetuate societal inequalities, unfairly favoring established global brands while marginalizing local ones. Using a curated dataset across four brand categories, we probe the behavior of LLMs in this space. We find a consistent pattern of bias in this space—both in terms of disproportionately associating global brands with positive attributes and disproportionately recommending luxury gifts for individuals in high-income countries. We also find LLMs are subject to country-of-origin effects which may boost local brand preference in LLM outputs in specific contexts.
%U https://aclanthology.org/2024.emnlp-main.707
%P 12695-12702

Markdown (Informal)

[“Global is Good, Local is Bad?”: Understanding Brand Bias in LLMs](https://aclanthology.org/2024.emnlp-main.707) (Kamruzzaman et al., EMNLP 2024)

• “Global is Good, Local is Bad?”: Understanding Brand Bias in LLMs (Kamruzzaman et al., EMNLP 2024)

ACL

• Mahammed Kamruzzaman, Hieu Nguyen, and Gene Kim. 2024. “Global is Good, Local is Bad?”: Understanding Brand Bias in LLMs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 12695–12702, Miami, Florida, USA. Association for Computational Linguistics.