Summary of Gemquad : Generating Multilingual Question Answering Datasets From Large Language Models Using Few Shot Learning, by Amani Namboori et al.
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning
by Amani Namboori, Shivam Mangale, Andy Rosenbaum, Saleh Soltan
First submitted to arxiv on: 14 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 The emergence of Large Language Models (LLMs) has enabled new possibilities for data generation across various domains while minimizing the need for extensive data collection and modeling techniques. Researchers have explored ways to use this generated synthetic data to optimize smaller student models for reduced deployment costs and lower latency in downstream tasks. However, ICL-generated data often suffers from low quality as the task specificity is limited with few examples used in ICL. This paper proposes GeMQuAD – a semi-supervised learning approach extending the WeakDAP framework applied to a dataset generated through ICL with just one example in the target language using AlexaTM 20B Seq2Seq LLM. The approach iteratively identifies high-quality data to enhance model performance, especially for low-resource multilingual setting in Extractive Question Answering task. The framework outperforms machine translation-augmented models by F1/EM points on MLQA dataset and surpasses the performance of English-only trained model by F1/EM points on same dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have made it possible to generate data for different domains without collecting lots of data. This paper talks about using this synthetic data to make smaller models better for use in real-world tasks. The problem is that the generated data isn’t always good quality because there aren’t enough examples used. To solve this, the researchers propose a new way to learn called GeMQuAD. It uses a special kind of LLM and only one example of labeled data to generate more data. This approach works better for languages with limited resources and is especially useful for question-answering tasks. |
Keywords
» Artificial intelligence » Question answering » Semi supervised » Seq2seq » Synthetic data » Translation
