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pro vyhledávání: '"Qi, Daiqing"'
Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilities of large language models (LLMs). However, is this effectiveness an
Externí odkaz:
http://arxiv.org/abs/2410.07589
Despite recent advances in the general visual instruction-following ability of Multimodal Large Language Models (MLLMs), they still struggle with critical problems when required to provide a precise and detailed response to a visual instruction: (1)
Externí odkaz:
http://arxiv.org/abs/2406.10839
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suffer many social issues induced by these systems. To make AI systems goo
Externí odkaz:
http://arxiv.org/abs/2308.12315
Federated learning is a technique that enables a centralized server to learn from distributed clients via communications without accessing the client local data. However, existing federated learning works mainly focus on a single task scenario with s
Externí odkaz:
http://arxiv.org/abs/2302.13001
Question answering is an effective method for obtaining information from knowledge bases (KB). In this paper, we propose the Neural-Symbolic Complex Question Answering (NS-CQA) model, a data-efficient reinforcement learning framework for complex ques
Externí odkaz:
http://arxiv.org/abs/2010.15881
Akademický článek
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Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Aug2024, Vol. 18 Issue 7, p1-53, 53p
Akademický článek
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Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; December 2022, Vol. 33 Issue: 12 p7921-7927, 7p