Data goes far beyond text—it is inherently multimodal, encompassing images, video, audio, and more, often in complex and unstructured formats. While the common method is to convert PDFs, scanned images, slides, and other documents into text, it is challenging to capture all information in text format, as shown in Figure 1. The loss of visual information in text motivated the development of…
]]>Efficient text retrieval is critical for a broad range of information retrieval applications such as search, question answering, semantic textual similarity, summarization, and item recommendation. It also plays a pivotal role in retrieval-augmented generation (RAG), a technique that enables large language models (LLMs) to access external context without modifying underlying parameters.
]]>The conversation about designing and evaluating Retrieval-Augmented Generation (RAG) systems is a long, multi-faceted discussion. Even when we look at retrieval on its own, developers selectively employ many techniques, such as query decomposition, re-writing, building soft filters, and more, to increase the accuracy of their RAG pipelines. While the techniques vary from system to system…
]]>Large language models (LLMs) are transforming the AI landscape with their profound grasp of human and programming languages. Essential for next-generation enterprise productivity applications, they enhance user efficiency across tasks like programming, copy editing, brainstorming, and answering questions on a wide range of topics. However, these models often struggle with real-time events and…
]]>Deep learning (DL) is the state-of-the-art solution for many machine learning problems, such as computer vision or natural language problems and it outperforms alternative methods. Recent trends include applying DL techniques to recommendation engines. Many large companies—such as AirBnB, Facebook, Google, Home Depot, LinkedIn, and Pinterest—share their experience in using DL for recommender…
]]>Recommender systems (RecSys) have become a key component in many online services, such as e-commerce, social media, news service, or online video streaming. However with the growth in importance, the growth in scale of industry datasets, and more sophisticated models, the bar has been raised for computational resources required for recommendation systems. To meet the computational demands…
]]>Recently, NVIDIA CEO Jensen Huang announced updates to the open beta of NVIDIA Merlin, an end-to-end framework that democratizes the development of large-scale deep learning recommenders. With NVIDIA Merlin, data scientists, machine learning engineers, and researchers can accelerate their entire workflow pipeline from ingesting and training to deploying GPU-accelerated recommenders (Figure 1).
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