Hyperparameter Optimization for Cross-Lingual Transfer Robustness in Multilingual LLM Benchmarks

Abstract

Abstract: Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. Several prior studies have shown that ChatGPT attains remarkable generation ability compared with existing models. However, the quantitative analysis of ChatGPT's understanding ability has been given little attention. In this report, we explore the understanding ability of ChatGPT by evaluating it on the most popular GLUE benchmark, and comparing it with 4 representative fine-tuned BERT-style models. We find that: 1) ChatGPT falls short in handling paraphrase and similarity

Research Question

Does optimizing hyperparameters for Matthews correlation coefficient improve cross-lingual transfer robustness in massively multilingual LLM benchmarks compared to F1 score?

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

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