The numeric-sensitive choice tuning (NumCT) approach, while effective in enhancing the model's understanding of financial text with numeric variables, may have some potential limitations:
Limited Numeric Range: NumCT generates numeric choices within a specific range around the true value. This may not cover all possible variations, leading to potential inaccuracies in predicting numeric values outside the generated choices.
Scalability: As the number of numeric variables and choices increases, the complexity of generating and managing these choices may become challenging and resource-intensive.
Overfitting: There is a risk of overfitting to the specific numeric choices generated during NumCT, which could impact the model's generalization to unseen data.
To further improve the NumCT approach, the following strategies could be considered:
Dynamic Range Generation: Implement a dynamic range generation mechanism that adapts to the specific numeric values in the text, allowing for a more comprehensive set of choices.
Diverse Numeric Choices: Introduce diversity in the generated numeric choices to cover a wider range of possibilities and enhance the model's robustness.
Regularization Techniques: Apply regularization techniques to prevent overfitting and ensure the model's performance on a broader range of numeric variables.
By addressing these limitations and incorporating these improvements, the NumCT approach can be further refined for more accurate and versatile handling of numeric variables in financial text.