Alapfogalmak
This study performs a thorough evaluation of the non-English capabilities of state-of-the-art large language models (GPT-3.5-Turbo, GPT-4, PaLM2, Gemini-Pro, Mistral, Llama2, and Gemma) by comparing them on the same set of multilingual datasets covering 83 languages, including low-resource African languages. It also includes multimodal datasets and compares the performance of LLaVA models, GPT-4-Vision, and Gemini-Pro-Vision. The experiments show that larger models like GPT-4, Gemini-Pro, and PaLM2 outperform smaller models on various tasks, notably on low-resource languages, with GPT-4 outperforming PaLM2 and Gemini-Pro on more datasets. The study also finds that several models are likely to be contaminated with multilingual evaluation benchmarks, necessitating approaches to detect and handle contamination while assessing the multilingual performance of large language models.
Kivonat
This study aims to perform a comprehensive evaluation of the non-English capabilities of state-of-the-art large language models (LLMs) by comparing their performance on a diverse set of multilingual datasets.
Key highlights:
- The benchmark covers 22 datasets spanning 83 languages, including many low-resource African languages.
- Nine new SOTA text LLMs are benchmarked, including PaLM2, Llama2, Mistral, Gemma, and Gemini-Pro, in addition to GPT-4 and GPT-3.5-Turbo.
- Multimodal LLMs like LLaVA, GPT-4-Vision, and Gemini-Pro-Vision are evaluated on two multilingual multimodal datasets.
- A thorough contamination study is conducted on both commercial and open-source LLMs, revealing that several models are likely contaminated with the evaluation datasets.
- The overall trends show that larger models like GPT-4, Gemini-Pro, and PaLM2 outperform smaller models, especially on low-resource languages. However, there are still significant performance gaps across language families and tasks.
The study highlights the importance of comprehensive multilingual evaluation and the need to address dataset contamination to accurately assess the capabilities of large language models.
Statisztikák
GPT-4 outperforms PaLM2 and Gemini-Pro on more datasets.
Larger models like GPT-4, Gemini-Pro, and PaLM2 outperform smaller models on various tasks, notably on low-resource languages.
Several models are likely to be contaminated with multilingual evaluation benchmarks, necessitating approaches to detect and handle contamination.
Idézetek
"GPT-4 outperforms PaLM2 and Gemini-Pro on more datasets."
"Larger models like GPT-4, Gemini-Pro, and PaLM2 outperform smaller models on various tasks, notably on low-resource languages."
"Several models are likely to be contaminated with multilingual evaluation benchmarks, necessitating approaches to detect and handle contamination."