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pro vyhledávání: '"NIU Tong"'
Accurate document retrieval is crucial for the success of retrieval-augmented generation (RAG) applications, including open-domain question answering and code completion. While large language models (LLMs) have been employed as dense encoders or list
Externí odkaz:
http://arxiv.org/abs/2411.00142
Despite significant advancements in the general capability of large language models (LLMs), they continue to struggle with consistent and accurate reasoning, especially in complex tasks such as mathematical and code reasoning. One key limitation is t
Externí odkaz:
http://arxiv.org/abs/2410.05318
Recently, many studies focus on utilizing large language models (LLMs) into educational dialogues. Especially, within liberal arts dialogues, educators must balance \textbf{H}umanized communication, \textbf{T}eaching expertise, and \textbf{S}afety-et
Externí odkaz:
http://arxiv.org/abs/2409.15461
As powerful pre-trained vision-language models (VLMs) like CLIP gain prominence, numerous studies have attempted to combine VLMs for downstream tasks. Among these, prompt learning has been validated as an effective method for adapting to new tasks, w
Externí odkaz:
http://arxiv.org/abs/2409.12011
Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning
Publikováno v:
International Conference on Autonomous Agents and Multiagent Systems 2024
Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies aimed at addressing challenges posed by complex systems and achieving various objectives. This process demands labor-intensive en
Externí odkaz:
http://arxiv.org/abs/2402.02388
Publikováno v:
Machine Intelligence Research 2024
Given the escalating intricacy and multifaceted nature of contemporary social systems, manually generating solutions to address pertinent social issues has become a formidable task. In response to this challenge, the rapid development of artificial i
Externí odkaz:
http://arxiv.org/abs/2401.13945
Publikováno v:
Anti-Corrosion Methods and Materials, 2024, Vol. 71, Issue 6, pp. 831-837.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ACMM-07-2024-3053
Autor:
Zhao, Wenting, Liu, Ye, Niu, Tong, Wan, Yao, Yu, Philip S., Joty, Shafiq, Zhou, Yingbo, Yavuz, Semih
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when solely relying on their internal knowledge, especially when answering questions that require less commonly known information. Ret
Externí odkaz:
http://arxiv.org/abs/2310.20170
Autor:
Nijkamp, Erik, Xie, Tian, Hayashi, Hiroaki, Pang, Bo, Xia, Congying, Xing, Chen, Vig, Jesse, Yavuz, Semih, Laban, Philippe, Krause, Ben, Purushwalkam, Senthil, Niu, Tong, Kryściński, Wojciech, Murakhovs'ka, Lidiya, Choubey, Prafulla Kumar, Fabbri, Alex, Liu, Ye, Meng, Rui, Tu, Lifu, Bhat, Meghana, Wu, Chien-Sheng, Savarese, Silvio, Zhou, Yingbo, Joty, Shafiq, Xiong, Caiming
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LLMs remain confined behind proprietary walls, hindering scientific prog
Externí odkaz:
http://arxiv.org/abs/2309.03450
Publikováno v:
The 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2023
Structural MRI and PET imaging play an important role in the diagnosis of Alzheimer's disease (AD), showing the morphological changes and glucose metabolism changes in the brain respectively. The manifestations in the brain image of some cognitive im
Externí odkaz:
http://arxiv.org/abs/2308.05655