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SIGIR 2024 Workshop on Generative Information Retrieval2024Large Language Models (LLMs) can be leveraged to improve performance in various stages of the search pipeline – the indexing stage, the query understanding stage, and the ranking or re-ranking stage. The latter two stages involve invoking a LLM during inference, adding latency in fetching the final ranked list of documents. Index enhancement, on the other hand, can be done in the indexing stage, in near
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SIGIR 2024 Workshop on eCommerce2024In a typical e-commerce setting, Content Ranking Optimization (CRO) mechanisms are employed to surface content on the search page to fulfill customers’ shopping missions. CRO commonly utilizes models such as contextual deep bandits model to independently rank content at different positions, e.g., one optimizer dedicated to organic search results and another to sponsored results. However, this regional optimization
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IEEE Signal Processing Letters2024Neural vocoders are now being used in a wide range of speech processing applications. In many of those applications, the vocoder can be the most complex component, so finding lower complexity algorithms can lead to significant practical benefits. In this work, we propose FARGAN, an autoregressive vocoder that takes advantage of long-term pitch prediction to synthesize high-quality speech in small subframes
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2024Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora to alleviate the issue. However, in many domains, texts are interconnected (e.g., academic papers in a bibliographic graph are linked by citations and co-authorships
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KDD 2024 Worksop on Fragile Earth: Generative and Foundational Models for Sustainable Development2024Last-mile carriers increasingly incorporate electric vehicles (EVs) into their delivery fleet to achieve sustainability goals. This goal presents many challenges across multiple planning spaces includ-ing but not limited to how to plan EV routes. In this paper, we address the problem of predicting energy consumption of EVs for Last-Mile delivery routes using deep learning. We demonstrate the need to move
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