这项工作不仅为动态策略性推理的定量评估设定了可靠的基准,同时K-Level Reasoning显着提高了LLMs的动态推理能力。 更多的实验细节与方法的讨论欢迎查看我们的论文 Paper page - K-Level Reasoning with Large Language Models (huggingface.co)。 欢迎大家的讨论、交流与分享~ ...
去年6 月底,我们在 arXiv 上发布了业内首篇多模态大语言模型领域的综述《A Survey on Multimodal Large Language Models》,系统性梳理了多模态大语言模型的进展和发展方向,目前论文引用 120+,开源 GitHub 项目获得8.3K Stars。自论文发布以来,我们收到了...
内容提示: American Economic Journal: Microeconomics 2024, 16(4): 40–76 https://doi.org/10.1257/mic.2021023740Consistent Depth of Reasoning in Level-k Models †By David J. Cooper, Enrique Fatas, Antonio J. Morales, and Shi Qi* Level-k models often assume that individuals employ a fi ...
去年6 月底,我们在 arXiv 上发布了业内首篇多模态大语言模型领域的综述《A Survey on Multimodal Large Language Models》,系统性梳理了多模态大语言模型的进展和发展方向,目前论文引用 120+,开源 GitHub 项目获得8.3K Stars。自论文发布以来,我们收到了很多读者非常宝贵的意见,感谢大家的支持! 去年以来,我们见证了...
去年6 月底,我们在 arXiv 上发布了业内首篇多模态大语言模型领域的综述《A Survey on Multimodal Large Language Models》,系统性梳理了多模态大语言模型的进展和发展方向,目前论文引用 120+,开源 GitHub 项目获得8.3K Stars。自论文发布以来,我们收到了很多读者非常宝贵的意见,感谢大家的支持!
去年6 月底,我们在 arXiv 上发布了业内首篇多模态大语言模型领域的综述《A Survey on Multimodal Large Language Models》,系统性梳理了多模态大语言模型的进展和发展方向,目前论文引用 120+,开源 GitHub 项目获得8.3K Stars。自论文发布以来,我们收到了很多读者非常宝贵的意见,感谢大家的支持!
https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models 去年以来,我们见证了以 GPT-4V 为代表的多模态大语言模型(Multimodal Large Language Model,MLLM)的飞速发展。为此我们对综述进行了重大升级,帮助大家全面了解该领域的发展现状以及潜在的发展方向。
Recently, large language models (LLMs) have demonstrated remarkable generalization capabilities across various applications, and hence they could be used for driving applications in general, and for driver behaviours modelling in particular. Lastly, machine learning models do not make any pre-assumptions...
PRM800K is a process supervision dataset containing 800,000 step-level correctness labels for model-generated solutions to problems from the MATH dataset.
Knowledge Graph Reasoning with Self-supervised Reinforcement Learning Official code for the following paper: PLACEHOLDER Setup Dependencies Use Docker Build the docker image docker build -< Dockerfile -t kg_ssrl:v1.0 Spin up a docker container and run experiments inside it. docker run --gpus all...