Data Analysis Solutions

Enhancing low-resource language data through advanced collection, balance strategies, and performance evaluation.

Data Analysis

Analyzing language corpora for balance-enhancing strategies and model training.

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Balance Strategies

Implementing strategies like data augmentation and transfer learning for improved model performance in low-resource languages.

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Model Training

Training GPT-4 on enhanced corpora and evaluating performance using BLEU and ROUGE metrics.

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Theoretical Contribution: Revealing the causes of data imbalance in low-resource language corpora and their impact on model performance, providing a new theoretical framework for corpus balance research.

Technical Contribution: Developing a set of corpus optimization tools for low-resource languages based on balance-enhancing strategies, advancing AI technology for low-resource languages.

Social Impact: Promoting language diversity preservation and cultural dissemination by improving the performance of low-resource language models, bridging the digital divide.

Model Optimization: Providing specific optimization suggestions for OpenAI’s models to better support low-resource language applications. This research will deepen our understanding of OpenAI’s models and their societal impact, driving AI technology toward greater inclusivity and fairness.