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What is Retrieval Augmented Generation (RAG)?

Pickl AI

This collaboration bridges the gap between static knowledge models 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.

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Commonsense Reasoning for Natural Language Processing

Probably Approximately a Scientific Blog

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.

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The Hallucination Problem of Large Language Models

Mlearning.ai

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.