LLaMA Beyond English: An Empirical Study on Language Capability Transfer
In recent times, substantial advancements have been witnessed in large
language models (LLMs), exemplified by ChatGPT, showcasing remarkable
proficiency across a range of complex tasks. However, many mainstream LLMs
(e.g. LLaMA) are pretrained on English-dominant corpus, which limits their
performance in other non-English languages. In this paper, we focus on how to
effectively transfer the capabilities of language generation and following
instructions to a non-English language. To answer this question, we conduct an
extensive empirical investigation based on LLaMA, accumulating over 1440 GPU
hours. We analyze the impact of key factors such as vocabulary extension,
further pretraining, and instruction tuning on transfer. To accurately assess
the model's level of knowledge, we employ four widely used standardized testing
benchmarks: C-Eval, MMLU, AGI-Eval, and GAOKAO-Bench. Furthermore, a
comprehensive evaluation of the model's response quality is conducted,
considering aspects such as accuracy, fluency, informativeness, logical
coherence, and harmlessness, based on LLM-Eval, a benchmarks consisting
instruction tasks from 17 diverse categories. Our evaluation results
demonstrate that comparable performance to state-of-the-art transfer models can
be achieved with less than 1% of the pretraining data, both in terms of
knowledge alignment and response quality. Furthermore, the experimental
outcomes across the thirteen low-resource languages also exhibit similar
trends. We anticipate that the conclusions revealed by the experiments will aid
the community in developing non-English LLMs.
6594c64e1adf6d577e5a3a11
Jun Zhao
6594c64e1adf6d577e5a3a12
Zhihao Zhang
6594c64e1adf6d577e5a3a13
Qi Zhang
6594c64e1adf6d577e5a3a14
Tao Gui
6594c64e1adf6d577e5a3a15
Xuanjing Huang
LLaMA Beyond English: An Empirical Study on Language Capability Transfer
In recent times, substantial advancements have been witnessed in large
language models (LLMs), exemplified by ChatGPT, showcasing remarkable
proficiency across a range of complex tasks. However, many mainstream LLMs
(e.g. LLaMA) are pretrained on English-dominant corpus, which limits their
performance in other non-English languages. In this paper, we focus on how to
effectively transfer the capabilities of language generation and following
instructions to a non-English language. To answer this question, we conduct an
extensive empirical investigation based on LLaMA, accumulating over 1440 GPU
hours. We analyze the impact of key factors such as vocabulary extension,
further pretraining, and instruction tuning on transfer. To accurately assess
the model's level of knowledge, we employ four widely used standardized testing
benchmarks: C-Eval, MMLU, AGI-Eval, and GAOKAO-Bench. Furthermore, a
comprehensive evaluation of the model's response quality is conducted,
considering aspects such as accuracy, fluency, informativeness, logical
coherence, and harmlessness, based on LLM-Eval, a benchmarks consisting
instruction tasks from 17 diverse categories. Our evaluation results
demonstrate that comparable performance to state-of-the-art transfer models can
be achieved with less than 1% of the pretraining data, both in terms of
knowledge alignment and response quality. Furthermore, the experimental
outcomes across the thirteen low-resource languages also exhibit similar
trends. We anticipate that the conclusions revealed by the experiments will aid
the community in developing non-English LLMs.
A comprehensive study on transferring English-dominant LLMs to non-English languages using LLaMA demonstrates that comparable performance can be achieved with minimal pretraining data across various language capabilities and response quality metrics.
large language models LLMs ChatGPT LLaMA vocabulary extension further pretraining instruction tuning C-Eval MMLU AGI-Eval GAOKAO-Bench LLM-Eval non-English languages low-resource languages