Zobrazeno 1 - 10
of 273
pro vyhledávání: '"Wu, JiaLong"'
Autor:
Wu, Siwei, Peng, Zhongyuan, Du, Xinrun, Zheng, Tuney, Liu, Minghao, Wu, Jialong, Ma, Jiachen, Li, Yizhi, Yang, Jian, Zhou, Wangchunshu, Lin, Qunshu, Zhao, Junbo, Zhang, Zhaoxiang, Huang, Wenhao, Zhang, Ge, Lin, Chenghua, Liu, J. H.
Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, merely increasing the number of model parameters yields diminishing perfo
Externí odkaz:
http://arxiv.org/abs/2410.13639
Autor:
Feng, Ningya, Pan, Junwei, Wu, Jialong, Chen, Baixu, Wang, Ximei, Li, Qian, Hu, Xian, Jiang, Jie, Long, Mingsheng
Lifelong user behavior sequences, comprising up to tens of thousands of history behaviors, are crucial for capturing user interests and predicting user responses in modern recommendation systems. A two-stage paradigm is typically adopted to handle th
Externí odkaz:
http://arxiv.org/abs/2410.02604
Conversational Query Reformulation (CQR) has significantly advanced in addressing the challenges of conversational search, particularly those stemming from the latent user intent and the need for historical context. Recent works aimed to boost the pe
Externí odkaz:
http://arxiv.org/abs/2407.01965
Autor:
Zhou, Wangchunshu, Ou, Yixin, Ding, Shengwei, Li, Long, Wu, Jialong, Wang, Tiannan, Chen, Jiamin, Wang, Shuai, Xu, Xiaohua, Zhang, Ningyu, Chen, Huajun, Jiang, Yuchen Eleanor
The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing "language agents", which are complex large language models (LLMs) pipelines involving both prompting techniques and tool usage methods. While languag
Externí odkaz:
http://arxiv.org/abs/2406.18532
Large Language Models (LLMs) demonstrate remarkable emergent abilities across various tasks, yet fall short of complex reasoning and planning tasks. The tree-search-based reasoning methods address this by surpassing the capabilities of chain-of-thoug
Externí odkaz:
http://arxiv.org/abs/2406.18200
Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, whereas radar point clouds retain only limited information. In this wor
Externí odkaz:
http://arxiv.org/abs/2406.10600
World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. However, the high demand for interactivity poses challenges in harnessing recent advancements in video gen
Externí odkaz:
http://arxiv.org/abs/2405.15223
Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depends on the selection and ordering of the optimization passes applied to the code. While most compilers rely on
Externí odkaz:
http://arxiv.org/abs/2404.16077
Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on
Externí odkaz:
http://arxiv.org/abs/2403.02738
Though Large Language Models (LLMs) have demonstrated the powerful capabilities of few-shot learning through prompting methods, supervised training is still necessary for complex reasoning tasks. Because of their extensive parameters and memory consu
Externí odkaz:
http://arxiv.org/abs/2403.01165