Empirical Analysis of Context Window Enhancement Methods in Retrieval Augmented Generation Models
Keywords:
Context window optimization, Retrieval Augmented Generation (RAG) systems, methodical analysis, different context window sizes (4k, 32k, and 128k tokens), optimization strategies, dynamic context window sizing, attention, 32k-token window, best configuration, highest level of performance, F1 score, Precision, Recall, larger windows, retrieval accuracy, quality of text generation.Abstract
This study analyzed context window optimization inthe Retrieval-Augmented Generation (RAG) systems.Through methodical analysis of different contextwindow sizes (4k, 32k, and 128k tokens) and the use of optimization strategies through dynamic context
References
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, H. and Wang, H., 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2(1), p.32.
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Published
2024-02-20
How to Cite
Rajesh Poojari. (2024). Empirical Analysis of Context Window Enhancement Methods in Retrieval Augmented Generation Models. Journal of Computational Analysis and Applications (JoCAAA), 33(2), 1114–1121. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5119
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