Summary of A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias, by Yuemei Xu et al.
A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias
by Yuemei Xu, Ling Hu, Jiayi Zhao, Zihan Qiu, Kexin XU, Yuqi Ye, Hanwen Gu
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Multilingual Large Language Models (MLLMs) have been developed to address the limitations in multilingual natural language processing, aiming to transfer knowledge from high-resource languages to low-resource languages. While significant progress has been made, challenges remain, including language imbalance, alignment, and bias. This paper provides a comprehensive analysis of MLLMs, exploring their evolution, key techniques, and multilingual capacities. The authors also examine the training corpora and datasets used for downstream tasks, as well as the state-of-the-art studies on multilingual representations. Additionally, the paper discusses bias in MLLMs, including its categories, evaluation metrics, and debiasing techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about big language models that can understand many languages. These models are important because they can help us translate text from one language to another more accurately. But there are still some big problems with these models, like when we try to use them for languages that don’t have much data. The authors of this paper want to understand how these models work and what makes them good or bad at certain tasks. They look at the training data, the methods used to make the models better, and even try to figure out why some models might be biased. |
Keywords
» Artificial intelligence » Alignment » Natural language processing
