VizCopilot integrates topic modeling with document visualization to support user oversight of retrieved context in enterprise chatbots, enabling detection of misalignments and adaptation of prompting strategies.
2025.Eval- uation of Retrieval-Augmented Generation: A Survey
4 Pith papers cite this work, alongside 104 external citations. Polarity classification is still indexing.
4
Pith papers citing it
104
external citations · OpenAlex
representative citing papers
VArify introduces a tree visualization to support human verification of GraphRAG evidence for LLM responses in food science, evaluated in a study with six domain experts.
Adding handwritten Cypher graph tools to an agentic RAG system roughly doubled factual-correctness precision and recall on MoNaCo complex questions and improved fine-grained truthfulness compared with vector-only RAG.
Cluster-based semantic chunking does not outperform fixed-size or recursive chunking for RAG on academic theses, and RAGAs faithfulness shows limited reliability in this setup.
citing papers explorer
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VizCopilot: Fostering Appropriate Reliance on Enterprise Chatbots with Context Visualization
VizCopilot integrates topic modeling with document visualization to support user oversight of retrieved context in enterprise chatbots, enabling detection of misalignments and adaptation of prompting strategies.
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VArify: A Visual Analytics System for Verifying Knowledge Enhanced Large Language Model Responses in Food Science
VArify introduces a tree visualization to support human verification of GraphRAG evidence for LLM responses in food science, evaluated in a study with six domain experts.
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Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
Adding handwritten Cypher graph tools to an agentic RAG system roughly doubled factual-correctness precision and recall on MoNaCo complex questions and improved fine-grained truthfulness compared with vector-only RAG.
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Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts
Cluster-based semantic chunking does not outperform fixed-size or recursive chunking for RAG on academic theses, and RAGAs faithfulness shows limited reliability in this setup.