The article discusses the continued relevance of Retrieval-Augmented Generation (RAG) in the face of advancements in large language models (LLMs). While LLMs have made remarkable progress, they still face challenges such as computational and memory constraints, fine-tuning difficulties, and limitations in maintaining contextual understanding across lengthy interactions.
The article highlights the importance of context in natural language processing tasks. Maintaining consistency, understanding complexities, and reducing hallucinations are key reasons why context is crucial. Large context windows can help LLMs consider more information and generate more relevant responses, but they come with computational costs.
To mitigate the cost of large context windows, the article suggests implementing caching, which can significantly improve response times, especially for repetitive tasks. The article also discusses the evolution of context windows, noting that as transformer models and data availability improve, and NLP tasks shift towards requiring broader contextual understanding, the size of text windows is likely to continue increasing.
Despite the advancements in LLMs, the article concludes that RAG remains a relevant and valuable approach, as it can provide the necessary context and grounding to generate more coherent and accurate responses, addressing the limitations of LLMs.
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by о medium.com 04-16-2024
https://medium.com/@InferenzTech/why-rag-still-matters-beyond-token-limits-in-llms-289d16a930afГлибші Запити