Numeric-Sensitive Large Language Model for Chinese Finance

Core Concepts

A novel numeric-sensitive large language model (NumLLM) is proposed to enhance the ability of language models in understanding financial text with numeric variables.

Abstract

The paper proposes a novel large language model called NumLLM for Chinese finance. The key contributions are:

1. Construction of a financial corpus called Fin-Textbooks from financial textbooks, which is essential for improving the numeric capability of language models during fine-tuning.

2. Development of a novel fine-tuning method with two individual low-rank adaptation (LoRA) modules. One module is for adapting the general-purpose language model to the financial domain, and the other is for enhancing the model's ability to understand financial text with numeric variables.

3. Experiments on a financial question-answering benchmark show that NumLLM can outperform existing financial language models, including both general-purpose and domain-specific models, on both numeric and non-numeric questions.

The paper first introduces related works on financial corpora and financial language models. It then details the architecture and training process of NumLLM, including the construction of Fin-Textbooks, continual pre-training, and the numeric-sensitive choice tuning (NumCT) method. Finally, the experimental results demonstrate the superior performance of NumLLM compared to various baselines.

Stats

The closing price of the Huaxia SSE 50 ETF fund was ¥2.649 on March 29, 2015.
The closing price of the SSE 50 ETF call option with an expiration in April and an exercise price of ¥2.250 was ¥0.406.
The original maturity period of small face value bonds is generally 7 to 15 years.

Quotes

"Although existing FinLLMs can achieve impressive performance in financial natural language understanding, they exhibit unsatisfactory performance in understanding financial text when numeric variables are involved in questions."
"We propose a novel LLM, called numeric-sensitive large language model (NumLLM), for Chinese finance."

Key Insights Distilled From

NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance

by Huan-Yi Su,K... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00566.pdf

Deeper Inquiries

How can the techniques proposed in this paper be adapted to finance in other languages beyond Chinese

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.

What are the potential limitations of the numeric-sensitive choice tuning (NumCT) approach, and how could it be further improved

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.

What other types of numeric-related tasks or applications could benefit from the numeric-sensitivity capabilities developed in this work

The numeric-sensitivity capabilities developed in this work can benefit various other numeric-related tasks or applications beyond financial question-answering. Some potential areas include:

Quantitative Analysis: NumLLM's ability to understand and process numeric variables can be valuable in quantitative analysis tasks such as financial forecasting, risk assessment, and trend analysis.

Data Interpretation: In fields like data science and analytics, NumLLM can assist in interpreting and analyzing numerical data sets, making sense of complex numerical patterns and relationships.

Scientific Research: Numeric-sensitive models can aid researchers in fields like physics, engineering, and biology by processing numerical data and assisting in simulations, experiments, and data interpretation.

Healthcare Applications: In healthcare, NumLLM's numeric capabilities can be utilized for medical data analysis, patient monitoring, and personalized treatment recommendations based on numerical health indicators.

By leveraging the numeric-sensitivity capabilities developed in this work, a wide range of applications requiring accurate handling and interpretation of numeric data can benefit from enhanced language models like NumLLM.