OmniEval introduces a comprehensive evaluation framework for financial RAG systems, combining automated data generation with human validation to assess both retrieval and generation performance.
https://arxiv.org/abs/2412.13018
🔧 Solution offered in the paper:
→ A matrix-based evaluation system categorizes queries into 5 task classes and 16 financial topics for structured assessment.
→ Multi-dimensional data generation combines GPT-4 and human annotation, achieving 87.47% acceptance ratio.
→ Multi-stage evaluation assesses both retrieval and generation performance.
→ Robust evaluation metrics use both rule-based and LLM-based approaches.
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💡 Key Insights:
→ RAG systems show significant performance variations across different financial topics
→ Current systems struggle most with multi-hop reasoning and conversational tasks
→ Domain-specific evaluation requires balanced assessment across diverse topics
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