Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages 事件

Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages arXiv:2510.05291v2 Announce Type: replace Abstract: As Large Language Models (LLMs) develop stronger multilingual capabilities, their sensitivity to culturally diverse entities becomes increasingly important. Prior work by Naous et al. (2024) has shown that LLMs often favor Western-associated entities in Arabic. Due to the lack of entity-centric multilingual benchmarks, it remains unclear if such biases also manifest in various