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This collaboration bridges the gap between static knowledgemodels and dynamic query resolution, ensuring relevance and fluency. As the need for more intelligent, responsive, and context-aware AI systems grows, RAG is positioned to play a key role in enhancing natural language processing (NLP) capabilities.
Visual question answering models guess the answer based on the frequency of answers for the same type of question in the training set, e.g. replying "2" to any "how many" question. Image captioning models often learn to recognize objects based solely on their typical environment , and fail to recognize them outside their typical environment.
When Contextual Knowledge Conflicts with the Parameteric Knowledge : Models have been shown to prioritize the parametric knowledge (acquired during pre-training) over the contextual knowledge which leads to hallucinations.
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