Summary of Hiro: Hierarchical Information Retrieval Optimization, by Krish Goel et al.
HIRO: Hierarchical Information Retrieval Optimization
by Krish Goel, Mahek Chandak
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Hierarchical Information Retrieval Optimization (HIRO), a novel querying approach that addresses the limitations of Large Language Models (LLMs) in handling information overload when leveraging hierarchical data structures for retrieval-augmented generation (RAG). HIRO employs Depth-First Search (DFS)-based recursive similarity score calculation and branch pruning to minimize context delivery to LLMs without informational loss, thereby improving performance on datasets such as NarrativeQA by 10.85%. This approach has significant implications for the development of more efficient and effective natural language processing techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HIRO is a new way to help computers better understand human language. Currently, these computers can only use what they learned from their training data. HIRO lets them find extra information to make them smarter. This helps computers do tasks like answering questions or generating text that makes sense. The best part is that HIRO does this without giving the computer too much information at once, so it doesn’t get overwhelmed. |
Keywords
» Artificial intelligence » Natural language processing » Optimization » Pruning » Rag » Retrieval augmented generation
