Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·14d ago·5 min read
FinRAG-12B: A Production-Validated Recipe for Grounded Question Answering in Banking
TL;DR
FinRAG-12B is a 12 billion parameter language model designed for grounded question answering in banking, achieving superior citation grounding and improved refusal rates compared to existing models. Deployed at over 40 financial institutions, it enhances query resolution by 7.1 percentage points.
✦ Why It Matters
Consider implementing FinRAG-12B to enhance your banking AI systems' accuracy and compliance today.
Key Takeaways
- Explore the data generation pipeline for efficient model training.
- Implement calibrated refusal to improve response safety.
- Utilize citation grounding to enhance answer verifiability.
- Deploy FinRAG-12B to boost query resolution in financial applications.
How It Works
FinRAG-12B employs a unique training approach that integrates LLM-as-a-Judge filtering to assess answer quality, citation annotation for verifiability, and curriculum learning to progressively train the model. This combination allows the model to learn effectively from a limited dataset while ensuring high performance in grounded question answering.