Marcin Zukowski

Came across an interesting research paper on optimizing Retrieval-Augmented Generation (RAG) systems. Key takeaways: * Fine-tune generators with relevant & random docs for robustness * Explore advanced hybrid retrieval methods & domain-specific techniques * Investigate SOTA reranking models & multi-stage approaches * Improve summarization with graph NNs, attention, & query-awareness * Incorporate domain knowledge via adaptation & ontologies * Evaluate on diverse datasets to ensure broad applicability By focusing on these aspects and iterating on RAG system design, we can significantly boost performance and efficiency, bringing us closer to highly effective retrieval-augmented generation for real-world applications. Excited to incorporate these techniques in upcoming projects Link to the original paper : https://lnkd.in/gp5eQyib Original Marktechpost Media Inc. article : https://lnkd.in/gXuJvXwB #NLProc #MachineLearning #AIResearch

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