arXiv:2601.03014v3 Announce Type: replace Abstract: Traditional Retrieval-Augmented Generation (RAG) effectively supports single-hop question answering with large language models but faces significant limitations in multi-hop question answering tasks, which require combining evidence from multiple documents. Existing chunk-based retrieval often provides irrelevant and logically incoherent context, leading to incomplete evidence chains and incorrect reasoning during answer generation. To address these challenges, we propose SentGraph, a sentence-level graph-based RAG framework that explicitly m
The increasing sophistication and widespread adoption of Large Language Models (LLMs) are pushing the boundaries of their capabilities, necessitating solutions for more complex tasks like multi-hop question answering.
This development enhances the reasoning capabilities of AI systems, moving them beyond simple information retrieval to complex problem-solving, which is critical for autonomous agents and advanced AI applications.
AI systems can now process and synthesize information from multiple sources more effectively, leading to more accurate and coherent responses to complex queries, thereby improving the reliability and utility of RAG frameworks.
- · AI Agent Developers
- · LLM Providers
- · Data Analytics Platforms
- · Enterprise AI Solutions
- · Traditional RAG frameworks
- · Information silos
- · Human knowledge workers performing multi-document synthesis
Improved performance and accuracy of AI systems in tasks requiring complex information synthesis.
Increased ability for AI agents to operate autonomously, reducing human oversight in complex decision-making processes.
Acceleration of AI integration into critical sectors, potentially leading to new forms of automation and intelligence-driven workflows.
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