Summary of Mindmerger: Efficient Boosting Llm Reasoning in Non-english Languages, by Zixian Huang et al.
MindMerger: Efficient Boosting LLM Reasoning in non-English Languages
by Zixian Huang, Wenhao Zhu, Gong Cheng, Lei Li, Fei Yuan
First submitted to arxiv on: 27 May 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 paper proposes a new method called MindMerger to improve Large Language Models’ (LLMs) reasoning capabilities in non-English languages. Unlike previous approaches that fine-tune or replace non-English inputs with English translation text, MindMerger leverages the built-in language understanding and reasoning abilities of LLMs by merging them with external multilingual models. The proposed method involves a two-step training scheme: first, embedding external capabilities into LLMs, and then training collaborative utilization of both internal and external capabilities. Experimental results on three multilingual reasoning datasets and one language understanding dataset demonstrate that MindMerger outperforms baselines, especially in low-resource languages, with an average accuracy improvement of 6.7% and 8.0% across all languages and low-resource languages on the MGSM dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computers better at understanding language by combining two types of artificial intelligence models together. The first type is called Large Language Models, which are good at understanding language but only work well with English. The second type is multilingual models that can understand many languages. The researchers propose a new method called MindMerger to combine these two types of models and make the Large Language Models better at understanding non-English languages. They test their method on several datasets and find that it works much better than previous methods, especially for languages where there isn’t as much data available. |
Keywords
» Artificial intelligence » Embedding » Language understanding » Translation
