Multi-hop Reasoning and Retrieval in Embedding Space: Leveraging Large Language Models with Knowledge
#multi-hop reasoning #embedding space #retrieval #Large Language Models #knowledge integration #AI #complex queries
📌 Key Takeaways
- Multi-hop reasoning involves connecting multiple pieces of information to answer complex queries.
- Retrieval in embedding space enhances the ability to find relevant knowledge efficiently.
- Large Language Models (LLMs) are leveraged to perform these reasoning tasks with integrated knowledge.
- The approach aims to improve accuracy in handling intricate questions requiring multiple inference steps.
📖 Full Retelling
arXiv:2603.13266v1 Announce Type: new
Abstract: As large language models (LLMs) continue to grow in size, their abilities to tackle complex tasks have significantly improved. However, issues such as hallucination and the lack of up-to-date knowledge largely remain unresolved. Knowledge graphs (KGs), which serve as symbolic representations of real-world knowledge, offer a reliable source for enhancing reasoning. Integrating KG retrieval into LLMs can therefore strengthen their reasoning by provi
🏷️ Themes
AI Reasoning, Knowledge Retrieval
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Original Source
arXiv:2603.13266v1 Announce Type: new
Abstract: As large language models (LLMs) continue to grow in size, their abilities to tackle complex tasks have significantly improved. However, issues such as hallucination and the lack of up-to-date knowledge largely remain unresolved. Knowledge graphs (KGs), which serve as symbolic representations of real-world knowledge, offer a reliable source for enhancing reasoning. Integrating KG retrieval into LLMs can therefore strengthen their reasoning by provi
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