Construction of a Japanese Financial-Specific Large Language Model through Continual Pre-training

מושגי ליבה

Constructing a Japanese financial-specific large language model through continual pre-training on domain-focused datasets, which outperforms the original model on Japanese financial benchmarks.

תקציר

The study aims to construct a Japanese financial-specific large language model (LLM) through continual pre-training. The authors first built Japanese financial-focused datasets containing around 8.1 million documents and 370 million tokens, covering various financial documents such as speeches, reports, and company profiles. They then employed a state-of-the-art Japanese LLM, rinna/nekomata-14b, as the base model and performed continual pre-training using the constructed datasets.
The authors evaluated the tuned model using Japanese financial benchmarks and by comparing the output quality with the original model. The results show that the tuned model outperformed the original model on all the benchmark tasks, indicating that the domain-specific continual pre-training was effective. The output comparison also revealed that the tuned model's outputs tend to be better than the original model's in terms of quality and informativeness, though the tuned model still had issues answering some financial domain-specific questions correctly.
The authors discuss that the domain-specific tuning is effective for LLMs, but the scope for future research includes instruction tuning, expanding the financial dataset coverage, and evaluating the domain-specific tuning for 100-billion-class-parameter models.

סטטיסטיקה

The Japanese financial-focused datasets contain around 8.1 million documents and 370 million tokens.
The tuned model achieved better performance than the original model on all the Japanese financial benchmark tasks.
The overall score of the tuned model is 0.4716, which is 0.0381 higher than the original model's score of 0.4335.

ציטוטים

"The tuned model's outputs tend to be better than the original model's outputs in terms of the quality and length of the answers."
"However, the tuned model still has issues to answer correctly for some questions."

תובנות מפתח מזוקקות מ:

Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training

by Masanori Hir... ב- arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10555.pdf

שאלות מעמיקות

What other domain-specific datasets could be used to further improve the performance of the Japanese financial-specific LLM?

To enhance the performance of the Japanese financial-specific Large Language Model (LLM), incorporating additional domain-specific datasets can be beneficial. Some potential datasets that could be utilized include:

Economic Indicators: Datasets containing information on economic indicators such as GDP growth rates, inflation rates, unemployment figures, and interest rates can provide valuable context for financial analysis and forecasting.

Market Data: Incorporating datasets on stock prices, trading volumes, market indices, and commodity prices can help the model understand market trends and dynamics, enabling more accurate predictions and insights.

Regulatory Documents: Including datasets of regulatory filings, policy updates, and compliance documents can assist the model in understanding the legal and regulatory framework of the financial industry, aiding in risk assessment and compliance tasks.

Company Financial Reports: Utilizing datasets of company financial statements, earnings reports, and analyst forecasts can enhance the model's ability to analyze and interpret financial data related to specific companies and industries.

Risk Management Data: Integrating datasets on risk metrics, credit ratings, and market risk factors can improve the model's capacity to assess and mitigate financial risks effectively.

By incorporating these diverse domain-specific datasets, the Japanese financial-specific LLM can gain a more comprehensive understanding of the financial domain, leading to enhanced performance in tasks such as sentiment analysis, financial planning, and risk assessment.

How can the tuned model's ability to answer domain-specific questions be improved, beyond just continual pre-training?

To further enhance the tuned model's capability to answer domain-specific questions in the financial sector, several strategies can be implemented beyond continual pre-training:

Fine-tuning with Instruction Datasets: Employing instruction tuning techniques where the model is trained on specific instructions or guidelines related to financial tasks can improve its ability to generate accurate and relevant responses to domain-specific queries.

Knowledge Distillation: Implementing knowledge distillation methods where the tuned model learns from a teacher model that excels in financial domain tasks can help transfer specialized knowledge and improve performance.

Multi-Task Learning: Training the model on multiple financial tasks simultaneously can enhance its versatility and proficiency in handling various types of financial queries, leading to more robust performance.

Interactive Learning: Incorporating interactive learning approaches where the model receives feedback from users on its responses and adjusts its answers accordingly can refine its understanding of complex financial concepts and improve accuracy.

Domain-Specific Evaluation Metrics: Developing domain-specific evaluation metrics tailored to financial tasks can provide more nuanced feedback on the model's performance and guide targeted improvements in answering domain-specific questions.

By implementing these advanced techniques in conjunction with continual pre-training, the tuned model can achieve higher levels of accuracy, relevance, and effectiveness in addressing domain-specific queries in the financial domain.

What are the potential applications and use cases of a Japanese financial-specific LLM in the real-world finance industry?

A Japanese financial-specific Large Language Model (LLM) holds significant potential for various applications and use cases in the real-world finance industry:

Automated Financial Analysis: The LLM can automate the analysis of financial reports, market trends, and economic indicators, providing insights for investment decisions, risk assessment, and strategic planning.

Customer Support and Chatbots: Deploying the LLM in customer support chatbots can enhance customer interactions by providing personalized financial advice, answering queries on products and services, and assisting with account management.

Compliance and Regulatory Compliance: The LLM can assist financial institutions in interpreting and complying with complex regulations by analyzing legal documents, identifying compliance issues, and ensuring adherence to regulatory requirements.

Risk Management: By leveraging the LLM for risk assessment and prediction, financial organizations can better evaluate market risks, credit risks, and operational risks, enabling proactive risk mitigation strategies.

Financial Forecasting: The LLM can be utilized for financial forecasting, predicting market trends, stock price movements, and economic indicators, aiding in decision-making processes and investment strategies.

Sentiment Analysis: Using the LLM for sentiment analysis on social media, news articles, and market reports can help gauge investor sentiment, market sentiment, and public perception, influencing trading strategies and market sentiment analysis.

Fraud Detection: Implementing the LLM for fraud detection and anomaly detection in financial transactions can enhance security measures, identify fraudulent activities, and protect against financial crimes.

Overall, a Japanese financial-specific LLM has the potential to revolutionize various aspects of the finance industry by streamlining processes, improving decision-making, and enhancing customer experiences through advanced language processing capabilities tailored to the financial domain.