How does the scaling of model size (e.g., 7B vs. 13B vs. 30B parameters) affect the cross-lingual transferabil

Abstract

Abstract: Multilingual large-scale Pretrained Language Models (PLMs) have been shown to store considerable amounts of factual knowledge, but large variations are observed across languages. With the ultimate goal of ensuring that users with different language backgrounds obtain consistent feedback from the same model, we study the cross-lingual consistency (CLC) of factual knowledge in various multilingual PLMs. To this end, we propose a Ranking-based Consistency (RankC) metric to evaluate knowledge consistency across languages independently from accuracy. Using this metric, we conduct an in-depth analys

Research Question

How does the scaling of model size (e.g., 7B vs. 13B vs. 30B parameters) affect the cross-lingual transferability of prosody detection in multimodal LLMs, as evaluated on non-English subsets of MMSU and other benchmarks like VoxCeleb or CREMA-D?

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