ESCARGOT AI Agent Outperforms RAG Methods, Enhances Reasoning with LLMs and Knowledge Graphs
ESCARGOT is an AI agent that integrates large language models (LLMs) with a dynamic Graph of Thoughts and biomedical knowledge graphs to enhance reasoning capabilities. The system aims to address common challenges in AI by improving output reliability and minimizing hallucinations. ESCARGOT reportedly outperforms industry-standard retrieval-augmented generation (RAG) methods, particularly excelling in open-ended questions that require high precision. Additionally, it provides greater transparency in its reasoning process, enabling users to vet both code and knowledge requests. This development is part of a broader trend in the AI field, where various entities are exploring advanced RAG techniques for improved information retrieval and generation.
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Jason H. Moore, PhDIn this new paper we address how to accurately query an ontology-based knowledgebase using LLMs with RAG & dynamic graph of thoughts. Blows ChatGPT out of the water https://t.co/OeMA0gEchu #bioinformatics #llms #nlp #ontologies #graphs #graphdatabase #graphofthoughts #knowledge
Data Science Dojo
💡 Revolutionizing AI with RAG! Jay Alammar breaks down how Retrieval-Augmented Generation (RAG) is transforming information retrieval in AI. Instead of cramming all the world’s knowledge into massive models, RAG dynamically pulls the right info at the right time—making AI… https://t.co/Tk4nbTVjXG
Towards Data Science What's the best way to evaluate RAG-based apps? @Thuwarakesh presents a clear and focused walkthrough of an approach to reduce hallucinations in your LLM outputs. https://t.co/POhi9rdlr3
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