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Do you want to improve AI in your language? Here's how you can help. 
Do you want to improve AI in your language? Here's how you can help. 
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I'm exploring different AI techniques for an upcoming project in journalism, and I wanted to test a cool idea by 
I'm exploring different AI techniques for an upcoming project in journalism, and I wanted to test a cool idea by 
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@davanstrien
davanstrien
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, Data is better together, which aims to foster a community of people to create DPO datasets in different languages.
, Data is better together, which aims to foster a community of people to create DPO datasets in different languages.
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This project gives the opportunity to explore various concepts: 
This project gives the opportunity to explore various concepts: 
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- Direct Preference Optimization (DPO)
- Direct Preference Optimization (DPO)
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- Synthetic data
- Synthetic data
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- Data annotation
- Data annotation
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- LLM as a judge
- LLM as a judge
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1️⃣ Take the Aya dataset of human-annotated prompt-completion pairs across 71 languages and filter it to include only those in the language you’re interested in. 
1️⃣ Take the Aya dataset of human-annotated prompt-completion pairs across 71 languages and filter it to include only those in the language you’re interested in. 
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2️⃣ Use distilabel from Argilla to generate a second response for each prompt and evaluate which response is best. 
2️⃣ Use distilabel from Argilla to generate a second response for each prompt and evaluate which response is best. 
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Basicaly, DPO datasets have a chosen and a rejected responses to a question, which helps align models on specific tasks.  To quote Daniel: "Currently, there are only a few DPO datasets available for a limited number of languages. By generating more DPO datasets for different languages, we can help to improve the quality of generative models in a wider range of languages."
Basicaly, DPO datasets have a chosen and a rejected responses to a question, which helps align models on specific tasks.  To quote Daniel: "Currently, there are only a few DPO datasets available for a limited number of languages. By generating more DPO datasets for different languages, we can help to improve the quality of generative models in a wider range of languages."
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3️⃣  Send this dataset and evaluations to the easy-to-use interface to evaluate the evaluations. 
3️⃣  Send this dataset and evaluations to the easy-to-use interface to evaluate the evaluations. 
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This is where you can help. :) You can rate the LLM evaluation of the prompt-responses pairs. For my example, I built a dataset in French. And without wanting to start a debate about homeopathy, the second result is clearly better in the example below!  
This is where you can help. :) You can rate the LLM evaluation of the prompt-responses pairs. For my example, I built a dataset in French. And without wanting to start a debate about homeopathy, the second result is clearly better in the example below!  
resource
https://huggingface.co/spaces/fdaudens/demo-aya-dpo-french
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fdaudens/demo-aya-dpo-french
https://huggingface.co/spaces/fdaudens/demo-aya-dpo-french
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The final dataset can be found here: 
The final dataset can be found here: 
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https://huggingface.co/datasets/fdaudens/aya_french_dpo
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fdaudens/aya_french_dpo
https://huggingface.co/datasets/fdaudens/aya_french_dpo
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To contribute to other languages and learn more about synthetic data, you can also produce datasets in the language of your choice! Read more about the project: 
To contribute to other languages and learn more about synthetic data, you can also produce datasets in the language of your choice! Read more about the project: 
link
https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md
https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md
Do you want to improve AI in your language? Here's how you can help. 

I'm exploring different AI techniques for an upcoming project in journalism, and I wanted to test a cool idea by @davanstrien, Data is better together, which aims to foster a community of people to create DPO datasets in different languages.

This project gives the opportunity to explore various concepts: 
- Direct Preference Optimization (DPO)
- Synthetic data
- Data annotation
- LLM as a judge

1️⃣ Take the Aya dataset of human-annotated prompt-completion pairs across 71 languages and filter it to include only those in the language you’re interested in. 

2️⃣ Use distilabel from Argilla to generate a second response for each prompt and evaluate which response is best. 

Basicaly, DPO datasets have a chosen and a rejected responses to a question, which helps align models on specific tasks.  To quote Daniel: "Currently, there are only a few DPO datasets available for a limited number of languages. By generating more DPO datasets for different languages, we can help to improve the quality of generative models in a wider range of languages."

3️⃣  Send this dataset and evaluations to the easy-to-use interface to evaluate the evaluations. 

This is where you can help. :) You can rate the LLM evaluation of the prompt-responses pairs. For my example, I built a dataset in French. And without wanting to start a debate about homeopathy, the second result is clearly better in the example below!  https://huggingface.co/spaces/fdaudens/demo-aya-dpo-french

The final dataset can be found here: https://huggingface.co/datasets/fdaudens/aya_french_dpo

To contribute to other languages and learn more about synthetic data, you can also produce datasets in the language of your choice! Read more about the project: https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md
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Daniel Vila
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