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Original Paper: https://arxiv.org/abs/2308.09658 By: Pengbo Hu, Ji Qi, Xingyu Li, Hong Li, Xinqi Wang, Bing Quan, Ruiyu Wang, Yi Zhou Abstract: There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning. This
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Original Paper: https://arxiv.org/abs/2308.10173 By: Zhixiao Qi, Yijiong Yu, Meiqi Tu, Junyi Tan, Yongfeng Huang Abstract: Currently, the construction of large language models in specific domains is done by fine-tuning on a base model. Some models also incorporate knowledge bases without the need for pre-training. This
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Original Paper: https://arxiv.org/abs/2303.15649 By: Senmao Li, Joost van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang Abstract: A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images. They either finetune
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Original Paper: https://arxiv.org/abs/2308.09687v2 By: Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, Torsten Hoefler Abstract: We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond
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Original Paper: https://arxiv.org/abs/2308.09718 By: Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao Abstract: The rapid advancement of deep learning models often attributes to their ability to leverage massive training data. In contrast, such privilege has not yet fully benefited
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Original Paper: https://arxiv.org/abs/2308.08614 By: Bin Lei, pei-Hung Lin, Chunhua Liao, Caiwen Ding Abstract: Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries. However, when facing complex problems that require multi-step logical reasoning, their accuracy dramatically decreases. Current research
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Original Paper: https://arxiv.org/abs/2308.01990 By: Rodrigo Pedro, Daniel Castro, Paulo Carreira, Nuno Santos Abstract: Large Language Models (LLMs) have found widespread applications in various domains, including web applications, where they facilitate human interaction via chatbots with natural language interfaces. Internally, aided by an LLM-integration middleware such
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Original Paper: https://arxiv.org/abs/2302.12449 By: Yun Zhu, Jianhao Guo, Siliang Tang Abstract: Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inherent gap between pretext and
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Original Paper: https://arxiv.org/abs/2308.07411 By: Edward Junprung Abstract: The final frontier for simulation is the accurate representation of complex, real-world social systems. While agent-based modeling (ABM) seeks to study the behavior and interactions of agents within a larger system, it is unable to faithfully capture the
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Original Paper: https://arxiv.org/abs/2308.06721 By: Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, Wei Yang Abstract: Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tricky to generate desired images
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Original Paper: https://arxiv.org/abs/2308.05596 By: Xinlei He, Savvas Zannettou, Yun Shen, Yang Zhang Abstract: The spread of toxic content online is an important problem that has adverse effects on user experience online and in our society at large. Motivated by the importance and impact of the
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Original Paper: https://arxiv.org/abs/2307.07415 By: Hong Sun, Xue Li, Yinchuan Xu, Youkow Homma, Qi Cao, Min Wu, Jian Jiao, Denis Charles Abstract: This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability
