Making LLMs finance-savvy
FINDAP introduces a systematic approach to adapt LLMs for finance through a comprehensive evaluation framework and novel training methods, enabling better domain-specific performance while preserving general capabilities.
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https://arxiv.org/abs/2501.04961
Original Problem 🤔:
Domain adaptation of LLMs faces challenges in balancing specialized knowledge with general capabilities. Financial LLMs often lack systematic evaluation criteria and effective training strategies across varying data configurations.
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Solution in this Paper 🔧:
→ FINDAP first identifies core capabilities required for financial domain expertise: domain knowledge, task performance, reasoning, and instruction-following.
→ It introduces a novel preference data distillation method leveraging process signals from a generative reward model.
→ The solution implements three-stage training: continual pre-training, instruction tuning, and preference alignment.
→ Training uses a mix of domain-specific and general knowledge to prevent catastrophic forgetting.
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Key Insights 💡:
→ Joint training of continual pre-training and instruction tuning outperforms sequential training
→ Parameter-efficient fine-tuning works well for task adaptation but requires full-model training for knowledge transfer
→ Process rewards from GenRM significantly improve reasoning capabilities
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Results 📊:
→ Outperforms all 8B baseline models across 91.13% financial tasks
→ Achieves state-of-the-art performance on CFA-Challenge with 55.56% accuracy
→ Maintains competitive MT-Bench score of 7.36 for instruction-following capability
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