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BERAG: Bayesian Ensemble Retrieval-Augmented Generation for Knowledge-based Visual Question Answering

A framework for retrieval-augmented generation that conditions language models on individual retrieved documents.

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BERAG: Bayesian Ensemble Retrieval-Augmented Generation for Knowledge-based Visual Question Answering

By Jinghong Chen, Jingbiao Mei, Guangyu Yang, Bill ByrnearXiv
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The paper proposes a new approach to question answering, called BERAG, which uses Bayesian ensemble methods to condition language models on individual documents. This allows for probabilistic re-ranking and clear attribution of document contribution.

The authors evaluate BERAG on knowledge-based visual question answering tasks and demonstrate substantial improvements over standard RAG.

Abstract

The paper proposes a new approach to question answering, called BERAG, which uses Bayesian ensemble methods to condition language models on individual documents. This allows for probabilistic re-ranking and clear attribution of document contribution. The authors evaluate BERAG on knowledge-based visual question answering tasks and demonstrate substantial improvements over standard RAG.

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bayesian ensemble retrieval-augmented generationlanguage modelsprobabilistic re-rankingdocument contribution attributionvisual question answeringLarge Language ModelsRetrieval & RAGSemantic InteroperabilityKnowledge Graphs
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