Measuring Massive Multitask Chinese Understanding
📰 ArXiv cs.AI
Learn to measure massive multitask Chinese understanding in large language models and improve their accuracy in various domains
Action Steps
- Develop a comprehensive test to assess multitask accuracy of large Chinese language models
- Evaluate models on four major domains: medicine, law, psychology, and education
- Implement 15 subtasks in medicine and 8 subtasks in education to measure model performance
- Compare results of different models to identify the best-performing ones in zero-shot settings
- Fine-tune models using the proposed test to improve their accuracy and capabilities
Who Needs to Know This
NLP researchers and developers can benefit from this knowledge to evaluate and enhance their Chinese language models, while data scientists can apply these methods to other languages and domains
Key Insight
💡 A well-designed test can help evaluate and improve the accuracy of large Chinese language models in various domains
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🚀 Measure massive multitask Chinese understanding in large language models with a new comprehensive test! 📊
Full Article
Title: Measuring Massive Multitask Chinese Understanding
Abstract:
arXiv:2304.12986v3 Announce Type: replace-cross Abstract: The development of large-scale Chinese language models is flourishing, yet there is a lack of corresponding capability assessments. Therefore, we propose a test to measure the multitask accuracy of large Chinese language models. This test encompasses four major domains, including medicine, law, psychology, and education, with 15 subtasks in medicine and 8 subtasks in education. We found that the best-performing models in the zero-shot set
Abstract:
arXiv:2304.12986v3 Announce Type: replace-cross Abstract: The development of large-scale Chinese language models is flourishing, yet there is a lack of corresponding capability assessments. Therefore, we propose a test to measure the multitask accuracy of large Chinese language models. This test encompasses four major domains, including medicine, law, psychology, and education, with 15 subtasks in medicine and 8 subtasks in education. We found that the best-performing models in the zero-shot set
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