EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
arXiv preprint arXiv:2309.11206 , year=
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A framework for inference-time knowledge graph construction and expansion improves factual accuracy in LLMs on three QA benchmarks by combining internal LLM knowledge with selective external retrieval.
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
LGPT and Early Query Fusion create flexible graph representations for LLMs, achieving 4.13% improvement on GraphQA without training the model.
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
citing papers explorer
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EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
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Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
A framework for inference-time knowledge graph construction and expansion improves factual accuracy in LLMs on three QA benchmarks by combining internal LLM knowledge with selective external retrieval.
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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
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Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models
LGPT and Early Query Fusion create flexible graph representations for LLMs, achieving 4.13% improvement on GraphQA without training the model.
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.