'Global is Good, Local is Bad?': Understanding Brand Bias in LLMs

Autor:	Kamruzzaman, Mahammed, Nguyen, Hieu Minh, Kim, Gene Louis
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Computational Engineering Finance and Science
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at EMNLP-2024 (main)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.13997 Zobrazit plný text záznamu View this record from Arxiv