Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Fantastic Paper from @GoogleDeepMind. Astute RAG enhances LLM performance by resolving conflicts between internal and external knowledge sources.
Fantastic Paper from @GoogleDeepMind.
Astute RAG enhances LLM performance by resolving conflicts between internal and external knowledge sources.
Original Problem 🔍:
RAG systems face challenges from imperfect retrieval, introducing irrelevant or misleading information. Knowledge conflicts between LLMs' internal knowledge and external sources undermine RAG effectiveness.
Solution in this Paper 🛠️:
• Astute RAG:
Adaptive generation of internal LLM knowledge
Iterative source-aware knowledge consolidation
Answer finalization based on information reliability
• Addresses knowledge conflicts explicitly
• Combines internal and external knowledge effectively
Key Insights from this Paper 💡:
• Imperfect retrieval is prevalent in real-world RAG (70% of retrieved passages lack direct answers)
• Knowledge conflicts exist in 19.2% of cases
• LLM internal knowledge and external sources have distinct advantages
• Effective combination of internal and external knowledge is crucial for reliable RAG
Results 📊:
• Outperforms baselines across datasets:
6.85% relative improvement on Claude
4.13% improvement on Gemini
• Only method matching/exceeding No-RAG performance in worst-case scenarios
• Resolves 80% of knowledge conflicts correctly
• Improves performance even when neither knowledge source alone is correct
Astute RAG differs from previous approaches by:
Explicitly incorporating LLM internal knowledge to recover from RAG failures
Using source-aware iterative consolidation to address knowledge conflicts
Maintaining performance gains under high retrieval quality while improving under low quality
Achieving near No-RAG performance in worst-case scenarios, unlike other methods
