Finally, we can systematically stress-test LLM's reading comprehension abilities at scale, exposing exactly where these systems fail when trying to find and use information from documents.
Paper - "RAGProbe: An Automated Approach for Evaluating RAG"
🔬 Introduces technique to generate question-answer variations triggering failures in RAG pipelines
📚 https://arxiv.org/pdf/2409.19019
Original Problem 🔍:
Evaluating RAG pipelines is mostly manual, lacking automated methods to generate diverse question-answer pairs and systematically assess performance.
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Solution in this Paper 💡:
• Introduces RAGProbe, an automated approach for RAG evaluation
• Defines 6 evaluation scenarios to generate diverse question-answer pairs:
- Retrieving numbers/dates from single documents
- Multiple-choice questions
- Combined questions from single/multiple documents
- Questions without answers in corpus
• Uses LLMs to generate scenario-specific questions and evaluate responses
• Implements end-to-end pipeline: Q&A generation, RAG execution, semantic evaluation
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Key Insights from this Paper 💡:
• Evaluation scenarios with combined questions had highest failure rates (78-91%)
• RAGProbe outperformed state-of-the-art in exposing RAG limitations
• RAG pipelines struggled across academic and open-domain datasets
• Automated evaluation enables continuous monitoring and CI/CD integration
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Results 📊:
• RAGProbe found 45-83% failures across 5 RAG pipelines (vs 19-71% for baseline)
• Generated 90-98% valid questions per dataset (vs 85-93% for baseline)
• Exposed 60%, 53%, 62% failure rates for Qasper, Google NQ, MS Marco datasets
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