Camellia is introduced, a benchmark for evaluating entity-centric cultural biases in nine Asian languages, spanning six Asian cultures, and it is found that LLMs can struggle with context understanding in some Asian languages, creating performance gaps between cultures in entity extraction.
Tarek Naous, Anagha Savit, Carlos Rafael Catalan et al.· arXiv.org· 2 citations· ⚡1
This work proposes a synthetic, simulation-driven framework for studying knowledge insertion in LLMs, and introduces {\sc ParallelEvents}, a benchmark of fictional yet realistic future worlds that generates coherent event trajectories for controlled evaluation, avoiding contamination while preserving consistency.
Jonathan Zheng, Zi-Rui Shao, Alan Ritter et al.· 0 citations
It is highlighted that effective cultural alignment requires context-conditional modeling rather than uniform debiasing, and a new direction for mitigating entity-centric cultural bias in LLMs is established.
SOLAR is proposed, an auxiliary objective for supervised fine-tuning that aligns soft-token representations across languages, using English as a pivot and strengthens final-layer cross-lingual similarity and substantially reduces language-cluster separability, suggesting that aligning soft-token representations helps p...
Jiayi He, Jungsoo Park, Wei Xu et al.· arXiv.org· 0 citations
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