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CodePlan: Repository-level Coding using LLMs and Planning
Paper • 2309.12499 • Published • 74 -
Octopus: Embodied Vision-Language Programmer from Environmental Feedback
Paper • 2310.08588 • Published • 35 -
SALMON: Self-Alignment with Principle-Following Reward Models
Paper • 2310.05910 • Published • 2 -
Lemur: Harmonizing Natural Language and Code for Language Agents
Paper • 2310.06830 • Published • 32
Anthony W Figueroa
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Exciting breakthrough in Retrieval-Augmented Generation (RAG): Introducing MiniRAG - a revolutionary approach that makes RAG systems accessible for edge devices and resource-constrained environments.
Key innovations that set MiniRAG apart:
Semantic-aware Heterogeneous Graph Indexing
- Combines text chunks and named entities in a unified structure
- Reduces reliance on complex semantic understanding
- Creates rich semantic networks for precise information retrieval
Lightweight Topology-Enhanced Retrieval
- Leverages graph structures for efficient knowledge discovery
- Uses pattern matching and localized text processing
- Implements query-guided reasoning path discovery
Impressive Performance Metrics
- Achieves comparable results to LLM-based methods while using Small Language Models (SLMs)
- Requires only 25% of storage space compared to existing solutions
- Maintains robust performance with accuracy reduction ranging from just 0.8% to 20%
The researchers from Hong Kong University have also contributed a comprehensive benchmark dataset specifically designed for evaluating lightweight RAG systems under realistic on-device scenarios.
This breakthrough opens new possibilities for:
- Edge device AI applications
- Privacy-sensitive implementations
- Real-time processing systems
- Resource-constrained environments
The full implementation and datasets are available on GitHub: HKUDS/MiniRAG
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