Summary of Zero-shot Cross-lingual Transfer in Instruction Tuning Of Large Language Models, by Nadezhda Chirkova et al.
Zero-shot cross-lingual transfer in instruction tuning of large language models
by Nadezhda Chirkova, Vassilina Nikoulina
First submitted to arxiv on: 22 Feb 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 proposed study delves into the realm of multilingual instruction following, investigating how language models can be trained to comprehend and respond to user prompts in various languages. The research focuses on zero-shot cross-lingual transfer, where a model is initially trained on English data and then tested on prompts from other languages. By examining different model configuration choices and hyperparameter tuning strategies, the study reveals that successful transfer does occur, albeit with limitations. While English-trained models can generate responses in other languages, their factuality and fluency may be compromised. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how language models can understand and respond to commands in different languages without being specifically trained for each language. Scientists want to know if a model trained only on English data can still work well with prompts from other languages. The study shows that this “zero-shot” transfer is possible, but it’s crucial to adjust the model settings and use enough training data. While these models can produce responses in other languages, they might not be entirely accurate or fluent. |
Keywords
» Artificial intelligence » Hyperparameter » Zero shot
