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pro vyhledávání: '"Qiu, Zexuan"'
Augmenting Large Language Models (LLMs) with retrieved external knowledge has proven effective for improving the factual accuracy of generated responses. Despite their success, retrieval-augmented LLMs still face the distractibility issue, where the
Externí odkaz:
http://arxiv.org/abs/2406.17519
Embedding-based retrieval serves as a dominant approach to candidate item matching for industrial recommender systems. With the success of generative AI, generative retrieval has recently emerged as a new retrieval paradigm for recommendation, which
Externí odkaz:
http://arxiv.org/abs/2404.14774
Developing Large Language Models (LLMs) with robust long-context capabilities has been the recent research focus, resulting in the emergence of long-context LLMs proficient in Chinese. However, the evaluation of these models remains underdeveloped du
Externí odkaz:
http://arxiv.org/abs/2403.03514
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript. Inspired by the success of
Externí odkaz:
http://arxiv.org/abs/2305.04824
Publikováno v:
EMNLP 2022
Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document distances. However, existing semantic hashing methods are mostly establi
Externí odkaz:
http://arxiv.org/abs/2210.17170
Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible. However, this requirement may force th
Externí odkaz:
http://arxiv.org/abs/2105.06138