Summary of Thank You, Stingray: Multilingual Large Language Models Can Not (yet) Disambiguate Cross-lingual Word Sense, by Samuel Cahyawijaya and Ruochen Zhang and Holy Lovenia and Jan Christian Blaise Cruz and Elisa Gilbert and Hiroki Nomoto and Alham Fikri Aji
Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word Sense
by Samuel Cahyawijaya, Ruochen Zhang, Holy Lovenia, Jan Christian Blaise Cruz, Elisa Gilbert, Hiroki Nomoto, Alham Fikri Aji
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study introduces a novel benchmark, StingrayBench, to evaluate the reliability of multilingual large language models (LLMs) beyond English. It demonstrates using false friends as a means to identify limitations in cross-lingual sense disambiguation and biases toward higher-resource languages. The authors propose new metrics for quantifying cross-lingual sense bias and comprehension based on their benchmark, contributing to more diverse and inclusive language modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how well computers can understand different languages and cultures. It creates a special test to see if big computer models are good at understanding words that look similar but mean different things in different languages. The researchers found that these models tend to do better with languages they were trained on, like English. They also created new ways to measure how well these models understand other languages. |
