Summary of M2lingual: Enhancing Multilingual, Multi-turn Instruction Alignment in Large Language Models, by Rishabh Maheshwary and Vikas Yadav and Hoang Nguyen and Khyati Mahajan and Sathwik Tejaswi Madhusudhan
M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models
by Rishabh Maheshwary, Vikas Yadav, Hoang Nguyen, Khyati Mahajan, Sathwik Tejaswi Madhusudhan
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper proposes a novel dataset called M2Lingual for instruction finetuning of Large Language Models (LLMs) across multiple languages and tasks. The dataset is constructed using a synthetic taxonomy, Evol, which generates complex multi-turn instructions from seed examples. This approach allows for the creation of a fully synthetic dataset that can be used to train LLMs of varying sizes. The authors demonstrate the effectiveness of M2Lingual by training LLMs and showcasing improved performance across a diverse set of languages. The dataset contains 182K total IFT pairs, covering 70 languages and 17+ NLP tasks. This paper contributes both the Evol taxonomy and guided generation code, as well as the M2Lingual dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding instructions. It’s like teaching a machine how to follow recipes or instructions from different languages. The researchers created a new way to make these instructions using a special system called Evol. They used this system to create a big dataset of instructions that can be used to train computers to understand many languages and tasks. This dataset is called M2Lingual and it has lots of examples (182,000) covering 70 languages and 17 different types of tasks. The goal is to make computers smarter and more helpful. |
Keywords
* Artificial intelligence * Nlp
