Summary of Chain-of-translation Prompting (cotr): a Novel Prompting Technique For Low Resource Languages, by Tejas Deshpande et al.
Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages
by Tejas Deshpande, Nidhi Kowtal, Raviraj Joshi
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 introduces Chain of Translation Prompting (CoTR), a strategy designed to improve language model performance in low-resource languages. CoTR restructures prompts by first translating input context from a low-resource language into English, then performing the specified task on the translated text. The method is demonstrated through a case study on Marathi, applying CoTR to tasks like sentiment analysis and hate speech classification. Results show significant improvements with CoTR, particularly for hate speech detection. This technique has potential applications in enhancing synthetic data generation for underrepresented languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps language models do better in languages that don’t have much information available. It does this by changing the way prompts are given to the model. The new method is called Chain of Translation Prompting (CoTR). It starts by translating what you want the model to do into a language the model knows well, like English. Then it asks the model to do that thing with the translated text. This helps the model do better in languages it doesn’t know as well. |
Keywords
» Artificial intelligence » Classification » Language model » Prompting » Synthetic data » Translation
