Summary of Boosting Zero-shot Crosslingual Performance Using Llm-based Augmentations with Effective Data Selection, by Barah Fazili et al.
Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data Selection
by Barah Fazili, Ashish Sunil Agrawal, Preethi Jyothi
First submitted to arxiv on: 15 Jul 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 The paper proposes using large language models (LLMs) to generate task-specific data for low-resource target languages through zero-shot prompting. This approach leverages the capabilities of LLMs to promote cross-lingual transfer learning. The authors suggest labeling LLM generations with a teacher model trained on the source language and employing simple data selection strategies based on the teacher’s label probabilities. These strategies help identify a representative subset of diverse generations that boost zero-shot accuracies while being efficient. Other design choices, such as using translations of source data and selecting suitable labels for LLM generations, also impact cross-lingual performance. The paper reports significant performance gains (up to 7.13 absolute points and 1.5 average points) across sentiment analysis and natural language inference tasks in Hindi, Marathi, Urdu, Swahili, and other target languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses special computer models called large language models to help languages that don’t have much data by generating new text examples. The authors teach these models how to generate more helpful text by using a “teacher” model trained on the same task. They also developed ways to pick the most useful generated text and tested it on different tasks like understanding emotions and making predictions about sentences. By doing so, they found that this approach can significantly improve performance in languages like Hindi, Marathi, Urdu, and Swahili. |
Keywords
» Artificial intelligence » Inference » Prompting » Teacher model » Transfer learning » Zero shot
