A framework for evaluating cultural bias and historical misconceptions in LLMs outputs

Large Language Models (LLMs), while powerful, often perpetuate cultural biases and historical inaccuracies from their training data, marginalizing underrepresented perspectives. To address these issues, we introduce a structured framework to systematically evaluate and quantify these deficiencies. O...

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Bibliographic Details
Main Authors: Moon-Kuen Mak, Tiejian Luo
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2025-09-01
Series:BenchCouncil Transactions on Benchmarks, Standards and Evaluations
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772485925000481
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