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README.md
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- 🔥🔥🔥 We have made SEA series models (7B) public !
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## Model Description
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The SEA-S model aims to integrate all reviews for each paper into one to eliminate redundancy and errors, focusing on the major advantages and disadvantages of the paper. Specifically, we first utilize GPT-4 to integrate multiple reviews of a paper into one (From [ECNU-SEA/SEA_data](https://huggingface.co/datasets/ECNU-SEA/SEA_data)) that is in a unified format and criterion with constructive contents, and form an instruction dataset for SFT. After that, we fine-tune [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) to distill the knowledge of GPT-4. Therefore, SEA-S provides a novel paradigm for integrating peer review data in an unified format across various conferences.
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```bibtex
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- 🔥🔥🔥 We have made SEA series models (7B) public !
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## Model Description
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**_This is the SEA-S model for content standardization, and the review model SEA-E can be found [here](https://huggingface.co/ECNU-SEA/SEA-E)._**
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The SEA-S model aims to integrate all reviews for each paper into one to eliminate redundancy and errors, focusing on the major advantages and disadvantages of the paper. Specifically, we first utilize GPT-4 to integrate multiple reviews of a paper into one (From [ECNU-SEA/SEA_data](https://huggingface.co/datasets/ECNU-SEA/SEA_data)) that is in a unified format and criterion with constructive contents, and form an instruction dataset for SFT. After that, we fine-tune [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) to distill the knowledge of GPT-4. Therefore, SEA-S provides a novel paradigm for integrating peer review data in an unified format across various conferences.
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```bibtex
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