Demystifying Domain-adaptive Post-training for Financial LLMs

Zixuan Ke, Yifei Ming, Xuan-Phi Nguyen, Caiming Xiong, Shafiq Joty·January 09, 2025

Summary

The paper introduces FINDAP, a systematic investigation into domain-adaptive post-training for finance, focusing on identifying core capabilities, designing an evaluation suite, and analyzing post-training stages. It proposes a novel preference data distillation method, leading to the Llama-Fin model, which outperforms others in financial tasks. Each post-training stage contributes to distinct capabilities, offering insights for domain adaptation of Large Language Models (LLMs).

Key findings

14

Introduction
Background
Overview of domain-adaptive post-training techniques
Importance of domain adaptation in finance
Objective
To systematically investigate domain-adaptive post-training for finance
To identify core capabilities, design an evaluation suite, and analyze post-training stages
Method
Data Collection
Selection of financial datasets
Gathering of diverse financial tasks
Data Preprocessing
Data cleaning and normalization
Splitting data into training, validation, and testing sets
Evaluation Suite
Designing a comprehensive evaluation framework
Metrics for assessing model performance in financial tasks
Post-Training Stages
Overview of each stage and its contribution
Analysis of the impact on model capabilities
Novel Preference Data Distillation Method
Methodology
Explanation of the preference data distillation method
How it enhances the Llama-Fin model
Implementation
Steps involved in applying the method
Integration with the Llama-Fin model
Results
Performance of the Llama-Fin model compared to others
Validation of the method's effectiveness
Insights for Domain Adaptation of Large Language Models (LLMs)
Core Capabilities
Identification of key capabilities developed through post-training stages
Adaptation Strategies
Recommendations for adapting LLMs to financial domains
Considerations for future research
Conclusion
Summary of FINDAP's contributions
Future Directions
Potential areas for further investigation
Expected advancements in domain-adaptive post-training for finance
Basic info
papers
computation and language
computational engineering, finance, and science
machine learning
artificial intelligence
Advanced features
Insights
What is the main focus of the paper regarding domain-adaptive post-training in finance?
How does the paper demonstrate the contribution of each post-training stage to distinct capabilities in domain adaptation for Large Language Models (LLMs)?
Can you describe the novel preference data distillation method proposed in the paper and its outcome?
What is the significance of the Llama-Fin model in the context of financial tasks?