TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes
This addresses the problem of creating cost-effective multilingual LLMs for low-resource languages, though it is incremental as it builds on existing instruction-tuning methods.
The paper tackles the high cost and limited benchmarks for multilingual LLMs by proposing TaCo, a translation-assisted chain-of-thought method, which doubled performance to 82% on a low-resource language in the Vicuna Benchmark compared to instruction tuning alone.
Creating multilingual LLMs poses a significant challenge. Pretraining or fine-tuning LLMs to adopt new languages is evidently very costly. Furthermore, there exist limitations concerning benchmark datasets and the metrics used to measure model performance in multilingual settings. This paper proposes cost-effective solutions to both aforementioned challenges. Firstly, we introduce the Multilingual Instruction-Tuning Dataset (MITS), comprised of Alpaca-52K, Dolly-15K, and Vicuna Benchmark translations into 132 languages. Secondly, we propose a new method called \emph{TaCo: Translation-Assisted Cross-Linguality}, which utilizes translations in a chain-of-thought process to instruction-tune LLMs on new languages through a curriculum-learning process. As a proof of concept, we experimented with the instruction-tuned Guanaco-33B model, performing further instruction tuning using our proposed TaCo method in three low-resource languages and one high-resource language. Our results indicate that the TaCo method impresses GPT-4 with an 82\% score for a low-resource language in the Vicuna Benchmark dataset, doubling the performance in contrast to instruction tuning alone. Furthermore, TaCo shows promise in creating multilingual LLMs, even for low-resource languages. We have released our datasets and model adapters\footnote{https://github.com/UNHSAILLab/TaCo} , encouraging the research community to utilize these resources to advance work on multilingual LLMs.