C4AI-Community/multilingual-reward-bench
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Yiyang Nan
nanyy1025
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Spent the weekend testing out some prompts with 🕵️♂️Mystery Bot🕵️♂️ on my mobile... exciting things are coming soon for the following languages:
🌐Arabic, Chinese, Czech, Dutch, English French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese!🌐
🌐Arabic, Chinese, Czech, Dutch, English French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese!🌐
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Several methods/models have recently been shared to generate synthetic data from minimal or no initial seeds, essentially creating data directly from raw text.
IMO, these approaches that rely on smaller models for synthetic data generation are quite valuable for scaling up synthetic data and democratizing access to creating domain-specific synthetic datasets.
I've compiled a collection of Gradio demos showcasing some of these methods here: davanstrien/synthetic-data-generation-demos-667573f248b97360ff3668a5
IMO, these approaches that rely on smaller models for synthetic data generation are quite valuable for scaling up synthetic data and democratizing access to creating domain-specific synthetic datasets.
I've compiled a collection of Gradio demos showcasing some of these methods here: davanstrien/synthetic-data-generation-demos-667573f248b97360ff3668a5
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Create synthetic instruction datasets using open source LLM's and bonito🐟!
With Bonito, you can generate synthetic datasets for a wide variety of supported tasks.
The Bonito model introduces a novel approach for conditional task generation, transforming unannotated text into task-specific training datasets to facilitate zero-shot adaptation of large language models on specialized data.
This methodology not only improves the adaptability of LLMs to new domains but also showcases the effectiveness of synthetic instruction tuning datasets in achieving substantial performance gains.
AutoBonito🐟: https://colab.research.google.com/drive/1l9zh_VX0X4ylbzpGckCjH5yEflFsLW04?usp=sharing
Original Repo: https://github.com/BatsResearch/bonito?tab=readme-ov-file
Paper: Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation (2402.18334)
With Bonito, you can generate synthetic datasets for a wide variety of supported tasks.
The Bonito model introduces a novel approach for conditional task generation, transforming unannotated text into task-specific training datasets to facilitate zero-shot adaptation of large language models on specialized data.
This methodology not only improves the adaptability of LLMs to new domains but also showcases the effectiveness of synthetic instruction tuning datasets in achieving substantial performance gains.
AutoBonito🐟: https://colab.research.google.com/drive/1l9zh_VX0X4ylbzpGckCjH5yEflFsLW04?usp=sharing
Original Repo: https://github.com/BatsResearch/bonito?tab=readme-ov-file
Paper: Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation (2402.18334)
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