Summary of Og-rag: Ontology-grounded Retrieval-augmented Generation For Large Language Models, by Kartik Sharma et al.
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models
by Kartik Sharma, Peeyush Kumar, Yunqing Li
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method, enhances language model (LLM)-generated responses by anchoring retrieval processes in domain-specific ontologies. Current LLMs struggle to adapt to specialized knowledge without fine-tuning or suboptimal retrieval methods. OG-RAG constructs a hypergraph representation of domain documents using domain-specific ontology and retrieves the minimal set of hyperedges that forms a precise, conceptually grounded context for the LLM. This method enables efficient retrieval while preserving complex relationships between entities. Applications include industrial workflows in healthcare, legal, and agricultural sectors, as well as knowledge-driven tasks like news journalism, investigative research, consulting, and more. OG-RAG increases recall of accurate facts by 55%, improves response correctness by 40% across four LLMs, and boosts fact-based reasoning accuracy by 27% compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OG-RAG helps computers understand complex information better! Right now, language models struggle to learn new things without extra training. OG-RAG changes this by using special “maps” of knowledge (ontologies) to help computers find the right answers. It works by creating a special graph that shows how different ideas are connected. Then, it uses this graph to pick out the most important information and create accurate responses. This is helpful for jobs like healthcare, law, and agriculture, where workers need to follow rules and procedures. OG-RAG makes computers better at finding facts and making smart decisions. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Rag » Recall » Retrieval augmented generation
