"Global is Good, Local is Bad?": Understanding Brand Bias in LLMs
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912047787147264 |
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| author | Kamruzzaman, Mahammed Nguyen, Hieu Minh Kim, Gene Louis |
| author_facet | Kamruzzaman, Mahammed Nguyen, Hieu Minh Kim, Gene Louis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13997 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | "Global is Good, Local is Bad?": Understanding Brand Bias in LLMs Kamruzzaman, Mahammed Nguyen, Hieu Minh Kim, Gene Louis Computation and Language Computational Engineering, Finance, and Science 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. |
| title | "Global is Good, Local is Bad?": Understanding Brand Bias in LLMs |
| topic | Computation and Language Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2406.13997 |