LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Reasoning

• URL: http://arxiv.org/abs/2412.12499v2
• Date: Mon, 17 Feb 2025 13:20:15 GMT
• Title: LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Reasoning
• Authors: Hongbin Zhang, Kehai Chen, Xuefeng Bai, Yang Xiang, Min Zhang,
• Abstract summary: Large language models (LLMs) have exhibited impressive multilingual reasoning capabilities, driven by extensive multilingual pre-training corpora and instruction fine-tuning data. A performance gap exists between high- and low-resource language reasoning tasks due to the language imbalance in the pre-training corpus. We propose LinguaLIFT, a two-stage instruction tuning framework for advancing low-resource language reasoning.
• Score: 28.288949710191158
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• Abstract: Large language models (LLMs) have exhibited impressive multilingual reasoning capabilities, driven by extensive multilingual pre-training corpora and instruction fine-tuning data. However, a performance gap exists between high- and low-resource language reasoning tasks due to the language imbalance in the pre-training corpus, which is exacerbated by evaluation bias in existing reasoning benchmarks lacking low-resource language coverage. To alleviate this issue, we propose LinguaLIFT, a two-stage instruction tuning framework for advancing low-resource language reasoning. LinguaLIFT employs a language alignment layer to capture multilingual alignment in a code-switched tuning way without requiring multilingual instruction or parallel data, thereby transferring the cross-lingual reasoning capabilities to low-resource languages through English-only instruction tuning data. To comprehensively evaluate the multilingual reasoning capabilities, we introduce the Multilingual Math World Problem (MMWP) benchmark, which spans 21 low-resource, 17 medium-resource, and 10 high-resource languages. Experimental results show that LinguaLIFT outperforms several competitive baselines across MMWP and four widely used benchmarks.

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