Why RAG Exists
Large language models (LLMs) are powerful tools for generating text, answering questions, and supporting a wide range of applications. However, they sometimes produce text that sounds plausible but is actually false or misleading. This issue is known as the hallucination problem. Hallucinations can lead to incorrect information being presented with high confidence, which is especially problematic in settings where accuracy and reliability are critical. Understanding why hallucinations happen is essential for developing more trustworthy AI systems.
Parametric memory refers to the information stored within the parameters of a language model during training. LLMs learn to encode vast amounts of knowledge in their weights, but this memory is limited in several ways: it cannot be updated after training, may not cover recent events, and struggles to recall specific facts not well represented in the training data.
To address these limitations, researchers have developed retrieval-augmented generation (RAG) approaches. The main motivation for RAG is to improve the factual accuracy, relevance, and timeliness of generated outputs. By combining the generative power of LLMs with the ability to retrieve information from external sources, RAG systems can ground their responses in up-to-date and verifiable knowledge. This reduces hallucinations, enables access to information beyond the model's training data, and supports applications that require current or specialized knowledge.
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Large language models (LLMs) are powerful tools for generating text, answering questions, and supporting a wide range of applications. However, they sometimes produce text that sounds plausible but is actually false or misleading. This issue is known as the hallucination problem. Hallucinations can lead to incorrect information being presented with high confidence, which is especially problematic in settings where accuracy and reliability are critical. Understanding why hallucinations happen is essential for developing more trustworthy AI systems.
Parametric memory refers to the information stored within the parameters of a language model during training. LLMs learn to encode vast amounts of knowledge in their weights, but this memory is limited in several ways: it cannot be updated after training, may not cover recent events, and struggles to recall specific facts not well represented in the training data.
To address these limitations, researchers have developed retrieval-augmented generation (RAG) approaches. The main motivation for RAG is to improve the factual accuracy, relevance, and timeliness of generated outputs. By combining the generative power of LLMs with the ability to retrieve information from external sources, RAG systems can ground their responses in up-to-date and verifiable knowledge. This reduces hallucinations, enables access to information beyond the model's training data, and supports applications that require current or specialized knowledge.
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