Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation
📰 ArXiv cs.AI
Learn how to improve Retrieval-Augmented Generation with executable multi-hop reasoning for better question answering
Action Steps
- Apply executable multi-hop reasoning to RAG systems to improve performance on multi-hop questions
- Use explicit intermediate states to reduce errors and improve interpretability
- Implement retrieval queries that are grounded in specific entities to prevent drift
- Test and evaluate the system on multi-hop question answering tasks to measure improvement
- Configure the system to handle errors and exceptions in the reasoning process
Who Needs to Know This
NLP engineers and researchers can benefit from this approach to enhance their question answering systems, especially for multi-hop questions
Key Insight
💡 Executable multi-hop reasoning can improve the performance of RAG systems on multi-hop questions by reducing errors and improving interpretability
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Executable multi-hop reasoning for RAG improves question answering #NLP #RAG
Key Takeaways
Learn how to improve Retrieval-Augmented Generation with executable multi-hop reasoning for better question answering
Full Article
Title: Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation
Abstract:
arXiv:2605.12975v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that current methods represent reasoning through free-form natural language, where intermediate states are implicit, retrieval queries can drift from intended entities, and errors
Abstract:
arXiv:2605.12975v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that current methods represent reasoning through free-form natural language, where intermediate states are implicit, retrieval queries can drift from intended entities, and errors
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