Enterprise AI Research Analysis
Federated Financial Reasoning Distillation: Training A Small Financial Expert by Learning From Multiple Teachers
Executive Impact Summary
This paper introduces a federated financial reasoning distillation framework to train compact, powerful financial expert Large Language Models (LLMs). It addresses the challenges of high computational costs and deployment complexities associated with state-of-the-art LLMs, particularly in the domain of financial reasoning. The core idea involves leveraging a 'federated' approach where insights from multiple teacher LLMs are combined and refined, guided by a sophisticated 'Judge LM' (GPT-4), to supervise a smaller student LM. The 7B distilled model achieves competitive performance on public financial QA datasets, surpassing other small LMs and rivaling larger teacher models like DeepSeek-V3. The framework emphasizes multi-source, quality-filtered supervision to impart strong reasoning capabilities and domain-specific knowledge, overcoming the computational and deployment costs of large LLMs.
72.96%
Accuracy on FinanceIQ
79.74%
Accuracy on FinEval
7B
Student Model Parameters
Deep Analysis & Enterprise Applications
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Overview
Methodology
Results
This research presents a novel framework for distilling knowledge from large, general-purpose LLMs into smaller, specialized financial expert models. It addresses the challenges of high computational costs and deployment complexities associated with state-of-the-art LLMs, particularly in the domain of financial reasoning.
The core idea involves leveraging a 'federated' approach where insights from multiple teacher LLMs are combined and refined, guided by a sophisticated 'Judge LM'. This ensures the student model learns from a diverse and high-quality set of reasoning traces, enabling it to perform complex financial analysis tasks efficiently.
The methodology comprises three key steps:
- Collecting Financial Rationale from Federated Teachers: Multiple reasoning and non-reasoning LLMs generate detailed Chain-of-Thoughts (CoTs) for financial Question-Answer pairs.
- Refining Rationale with a Judge: A 'Judge LLM' (e.g., GPT-4) evaluates the quality and correctness of generated CoTs, assigning scores. CoTs from top-performing teachers or those with high scores are prioritized. Low-quality CoTs are rejected.
- Instruction Tuning for the Student LM: The refined, high-quality CoT dataset is used to fine-tune a smaller student LLM (e.g., Qwen2.5-7B) using Instruction Supervised Finetuning, imbuing it with strong financial reasoning capabilities.
Experimental results on FinEval and FinanceIQ datasets demonstrate the effectiveness of the proposed framework. The 7B distilled student model achieves competitive performance, outperforming other small LMs and closely matching or even surpassing some larger teacher LLMs in financial reasoning tasks. Notably, the multi-teacher, quality-controlled distillation framework proves superior to single-teacher or unjudged distillation approaches.
The study also reveals that non-reasoning LLMs like GPT-4 can serve as effective 'Judge LMs' due to their strong general judgment abilities, even if they lack explicit step-by-step reasoning capabilities themselves. The refined financial CoT dataset is open-sourced to foster future research.
72.96%
Achieved accuracy on FinanceIQ with the 7B student model, demonstrating competitive performance against larger LLMs.
Federated Financial Reasoning Distillation Process
Collect Financial Rationale from Diverse Teacher LMs
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Judge LM Refines & Filters CoTs for Quality
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Instruction Tune Student LM with High-Quality CoTs
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Deploy Compact Financial Expert LM
| Model Type | Key Strengths | Challenges |
|---|---|---|
| Reasoning LLMs (e.g., DeepSeek-R1) |
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| Non-Reasoning LLMs (e.g., GPT-4) |
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| Distilled Student LLMs (Our Model) |
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Impact of Judge-Filtered CoTs on Student Performance
A crucial finding highlights the significant improvement gained by filtering Chain-of-Thought (CoT) rationales using a Judge LM. For instance, the DeepSeek-R1-Distill-Qwen2.5-7B model's accuracy on FinanceIQ increased from 71.54% (without Judge) to 72.55% (with GPT-4 as Judge). This demonstrates that not all generated CoTs are accurate or equally effective for student learning. The Judge LM plays a vital role in curating a high-quality supervision signal, leading to more robust and accurate student models. The study found that even non-reasoning LLMs like GPT-4, despite not generating step-by-step reasoning themselves, excelled at evaluating the coherence and correctness of provided CoTs, acting as an effective filter for quality.
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Estimated Annual Savings
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Strategic Implementation Roadmap
Our phased approach ensures a smooth and effective integration of advanced AI, tailored to your enterprise's unique needs and goals.
Phase 1: Foundation & Data Curation
Benchmark existing LLMs on financial reasoning tasks and establish a robust multi-teacher CoT generation pipeline, including the integration of Judge LMs for quality control.
Phase 2: Distillation & Fine-Tuning
Iteratively fine-tune the student LM using the refined, high-quality financial reasoning CoTs. Implement and optimize knowledge distillation techniques for domain-specific expertise transfer.
Phase 3: Evaluation & Refinement
Comprehensive evaluation of the distilled financial expert LLM on diverse financial QA datasets. Refinement of distillation strategies and prompt engineering for optimal performance.
Phase 4: Deployment & Integration
Prepare the compact financial expert LLM for practical deployment in enterprise environments, focusing on efficiency, scalability, and integration with existing financial systems.
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