The techniques proposed in the paper for the numeric-sensitive large language model (NumLLM) can be adapted to finance in other languages beyond Chinese by following a similar methodology with language-specific adaptations. Here are some steps to adapt the techniques to other languages:
Data Collection: Just like in the paper where a financial corpus was constructed from Chinese financial textbooks, the first step would be to gather financial text data in the target language. This could include financial news articles, reports, and other relevant sources.
Preprocessing: Preprocess the financial text data by filtering out non-financial content, refining the text to focus on financial knowledge, and calibrating numeric-related formatting issues.
Fine-Tuning: Train the large language model on the financial corpus using continual pre-training and numeric-sensitive choice tuning (NumCT) techniques. This involves adapting the model to the specific financial domain and enhancing its understanding of numeric variables in the text.
Evaluation: Evaluate the performance of the adapted model on financial question-answering benchmarks in the target language to assess its effectiveness in understanding financial text with numeric variables.
Iterative Improvement: Continuously refine the model based on feedback and performance evaluation to enhance its capabilities for finance in the target language.
By following these steps and making language-specific adjustments as needed, the techniques developed in the paper can be effectively applied to finance in other languages.