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Notes on retrieval-augmented generation
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KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs
The study proposes a new method called KGCaRe for answering complex conditional questions using large language models (LLMs) and retrieval-augmented generation (RAG). This approach combines neural retrieval with…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The paper introduces a new approach called READ (Reliable Embedding-free Agentic Document-search) for retrieving information from long documents such as financial statements and audit reports. The traditional method of…
A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility
Researchers have developed a platform called APS-RAG for scientific facilities like the Advanced Photon Source (APS). The platform aims to make the vast amount of institutional knowledge accessible through natural…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors present GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating updates across multiple agents in the pipeline. The system uses feedback from an Evaluator to update…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The paper introduces GRADRAG, a framework that improves Retrieval-Augmented Generation (RAG) systems by coordinating improvements across the pipeline. Instead of optimizing components in isolation, GRADRAG models the…
Earthquaker-AI: A Retrieval-Augmented Generation Framework with Rubric-Based Assessment for Primary School Earthquake Education
The paper introduces Earthquaker-AI, a hybrid educational framework that uses Lego WeDo2 robotics for earthquake simulations and an AI assistant for guided learning and feedback. The system aims to enhance earthquake…
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