Summary of Lingualift: An Effective Two-stage Instruction Tuning Framework For Low-resource Language Reasoning, by Hongbin Zhang et al.
LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Reasoning
by Hongbin Zhang, Kehai Chen, Xuefeng Bai, Yang Xiang, Min Zhang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel framework called LinguaLIFT to improve multilingual reasoning capabilities in large language models (LLMs). The current state-of-the-art LLMs exhibit impressive multilingual reasoning skills due to extensive pre-training on multilingual corpora and instruction fine-tuning. However, a performance gap exists between high- and low-resource languages, largely due to evaluation biases in existing benchmarks. To bridge this gap, LinguaLIFT employs a language alignment layer that captures multilingual alignment through code-switched tuning without requiring parallel data or multilingual instructions. This framework is evaluated on 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very good at understanding many languages! However, they struggle when it comes to smaller languages. To help them do better, researchers came up with a new way to teach them called LinguaLIFT. It’s like giving them a special instruction book that helps them understand how to translate between languages without needing lots of extra language data. They tested this new method on 48 different languages and found that it works really well! This is important because it can help us use computers to communicate with people who speak smaller languages, which can be very helpful for many people around the world. |
Keywords
* Artificial intelligence * Alignment * Fine tuning
