
The author explores how Retrieval-Augmented Generation (RAG) can counter hallucinations in language models by integrating external knowledge with prompts, highlighting the need for more robust solutions to ensure reliability. The main thesis is that while RAG can increase accuracy, it can still be misled when prompts contradict the model's pre-trained understanding.


coremsg

The-Double-Edged-Sword-of-Retrieval-Augmented-Chatbots


The Double-Edged Sword of Retrieval-Augmented Chatbots


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Retrieval-Augmented Generation (RAG) can enhance the accuracy of language models but faces challenges with hallucinations, emphasizing the need for robust solutions.


ragged-edges-the-double-edged-sword-of-retrieval-augmented-chatbots


RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots



Hallucinations in large language models are a prevalent issue that requires a cohesive framework and precise definitions within the NLP research community.


hallucinations-in-natural-language-processing-definitions-frameworks-and-societal-implications


Hallucinations in Natural Language Processing: Definitions, Frameworks, and Societal Implications
