PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering
📰 ArXiv cs.AI
PAR$^2$-RAG is a new approach for multi-hop question answering that combines planned active retrieval and reasoning to improve performance
Action Steps
- Combine planned retrieval with active learning to adapt to changing intermediate evidence
- Use iterative refinement to improve recall and reduce downstream errors
- Implement a reasoning component to combine evidence from multiple documents
Who Needs to Know This
NLP researchers and AI engineers on a team can benefit from this approach to improve the performance of their question answering systems, and product managers can leverage this technology to develop more accurate and reliable QA products
Key Insight
💡 Combining planned active retrieval and reasoning can improve the performance of multi-hop question answering systems
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🤖 PAR$^2$-RAG: a new approach to multi-hop question answering that combines planned active retrieval and reasoning #LLMs #NLP
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