Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word Sense

• URL: http://arxiv.org/abs/2410.21573v2
• Date: Wed, 30 Oct 2024 11:56:17 GMT
• Title: Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word Sense
• Authors: Samuel Cahyawijaya, Ruochen Zhang, Holy Lovenia, Jan Christian Blaise Cruz, Elisa Gilbert, Hiroki Nomoto, Alham Fikri Aji,
• Abstract summary: We introduce a novel benchmark for cross-lingual sense disambiguation, StingrayBench. We collect false friends in four language pairs, namely Indonesian-Malay, Indonesian-Tagalog, Chinese-Japanese, and English-German. In our analysis of various models, we observe they tend to be biased toward higher-resource languages.
• Score: 30.62699081329474
• License:
• Abstract: Multilingual large language models (LLMs) have gained prominence, but concerns arise regarding their reliability beyond English. This study addresses the gap in cross-lingual semantic evaluation by introducing a novel benchmark for cross-lingual sense disambiguation, StingrayBench. In this paper, we demonstrate using false friends -- words that are orthographically similar but have completely different meanings in two languages -- as a possible approach to pinpoint the limitation of cross-lingual sense disambiguation in LLMs. We collect false friends in four language pairs, namely Indonesian-Malay, Indonesian-Tagalog, Chinese-Japanese, and English-German; and challenge LLMs to distinguish the use of them in context. In our analysis of various models, we observe they tend to be biased toward higher-resource languages. We also propose new metrics for quantifying the cross-lingual sense bias and comprehension based on our benchmark. Our work contributes to developing more diverse and inclusive language modeling, promoting fairer access for the wider multilingual community.

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