题目:Large Multimodal Agents: A Survey 作者:Junlin Xie♣♡∗ Zhihong Chen♣♡∗ Ruifei Zhang♣♡ Xiang Wan♣ Guanbin Li♠† 单位:♡香港中文大学深圳 ♣深圳大数据研究所 ♠中山大学 链接:https://arxiv.org/abs/2402.15116...
大型多模态代理-LMAs-综述 | 一篇由LLM驱动的多模态代理的综述文章-Large Multimodal Agents: A Survey。 - 详细介绍了LMAs的四个核心要素,包括感知、规划、行动和记忆。 - 将现有研究分类为四种类型:类型I-闭源LLMs作为无长期记忆的规划者;类型II-微调的LLMs作为无长期记忆的规划者;类型III-具有间接长期记忆的...
AppAgent: multimodal agents as smartphone users. 2023, arXiv preprint arXiv: 2312.13771 Madaan A, Tandon N, Clark P, Yang Y. Memory-assisted prompt editing to improve GPT-3 after deployment. In: Proceedings of 2022 Conference on Empirical Methods in Natural Language Processing. 2022, 2833–...
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Large Models and Multimodal: A Survey of Cutting-Edge Approaches to Knowledge Graph Completiondoi:10.1007/978-981-97-5672-8_14The critical task of knowledge graph completion (KGC) cannot be overlooked when it comes to the evolution and application of new-generation knowledge graphs. With the ...
The advent of LLMs, particularly multimodal models, has ushered in a new era of GUI automation. They have demonstrated exceptional capabilities in natural language understanding, code generation, and visual processing. This has paved the way for a new generation of LLM-brained GUI agents capable ...
Large Models and Multimodal: A Survey of Cutting-Edge Approaches to Knowledge Graph Completion 来自 Springer 喜欢 0 阅读量: 19 作者:M Wu,Y Gong,H Lu,B Li,K Wang,Y Zhou,L Li 摘要: The critical task of knowledge graph completion (KGC) cannot be overlooked when it comes to the evolution...
The first survey for Multimodal Large Language Models (MLLMs). ✨ Welcome to add WeChat ID (wmd_ustc) to join our MLLM communication group! 🌟 🔥🔥🔥MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models ...
A Survey on Benchmarks of Multimodal Large Language Models - Timothyxxx/Evaluation-Multimodal-LLMs-Survey
Appagent: Multimodal agents as smartphone users. arXiv preprint arXiv:2312.13771, 2023 97. Madaan A, Tandon N, Clark P, Yang Y. Memory-assisted prompt editing to improve GPT-3 after deployment. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2022 ...