Yh Hust Github

Yh Hust Github Yh hust has 4 repositories available. follow their code on github. Experimental results demonstrate the superiority and high efficiency of our approach over other models on the task of long multimodal pdf understanding, surpassing proprietary products by an average of 8.6% on f1. our code and dataset will be released at github yh hust pdf wukong.

Github Yh Hust Pdf Wukong Arxiv Pdf Wukong A Large Multimodal First, we propose visuriddles, a benchmark for avr, featuring tasks meticulously constructed to assess models' reasoning capacities across five core dimensions and two high level reasoning categories. 2024年10月，华中科技大学白翔团队与华为研究人员合作，推出了基于 国产芯片 的多模态文档大模型 pdf wukong。 这一创新成果针对复杂多页pdf文档问答场景，提出了两项关键技术：端到端稀疏采样机制和多页pdf问答高质量数据生成方法。 这些技术突破使得输入长度有限的多模态大模型能够有效处理理论上无限长的pdf文档，实现深度理解和精准问答。 pdf wukong不仅解决了现有多模态大模型难以处理长pdf文档的技术难题，其性能还超越了多个知名的国际闭源商业产品，该成果展示了国产芯片在支持复杂大模型应用方面的实力。 在大模型技术快速发展的今天，处理复杂的多页pdf文档仍然是一个重大挑战。. First, we propose visuriddles, a benchmark for avr, featuring tasks meticulously constructed to assess models' reasoning capacities across five core dimensions and two high level reasoning categories. Pdf wukong project is intended for non commercial use only. for commercial inquiries, please contact haoyan at [email protected]. 【arxiv】pdf wukong: a large multimodal model for efficient long pdf reading with end to end sparse sampling.

Github Yh Hust Pdf Wukong Arxiv Pdf Wukong A Large Multimodal First, we propose visuriddles, a benchmark for avr, featuring tasks meticulously constructed to assess models' reasoning capacities across five core dimensions and two high level reasoning categories. Pdf wukong project is intended for non commercial use only. for commercial inquiries, please contact haoyan at [email protected]. 【arxiv】pdf wukong: a large multimodal model for efficient long pdf reading with end to end sparse sampling. Our extensive experimental results on visuriddles empirically validate that fine grained visual perception is the principal bottleneck and our synthesis framework markedly enhances the performance of contemporary mllms on these challenging tasks. our code and dataset will be released at github yh hust visuriddles. My research focuses on natural language processing (nlp) with an emphasis on large language models (llms) for low resource languages and efficient llms. First, we propose visuriddles, a benchmark for avr, featuring tasks meticulously constructed to assess models' reasoning capacities across five core dimensions and two high level reasoning categories. Our extensive experimental results on visuriddles empirically validate that fine grained visual perception is the principal bottleneck and our synthesis framework markedly enhances the performance of contemporary mllms on these challenging tasks. our code and dataset will be released at \url { github yh hust visuriddles}.

Github Yh Hust Pdf Wukong Arxiv Pdf Wukong A Large Multimodal Our extensive experimental results on visuriddles empirically validate that fine grained visual perception is the principal bottleneck and our synthesis framework markedly enhances the performance of contemporary mllms on these challenging tasks. our code and dataset will be released at github yh hust visuriddles. My research focuses on natural language processing (nlp) with an emphasis on large language models (llms) for low resource languages and efficient llms. First, we propose visuriddles, a benchmark for avr, featuring tasks meticulously constructed to assess models' reasoning capacities across five core dimensions and two high level reasoning categories. Our extensive experimental results on visuriddles empirically validate that fine grained visual perception is the principal bottleneck and our synthesis framework markedly enhances the performance of contemporary mllms on these challenging tasks. our code and dataset will be released at \url { github yh hust visuriddles}.

Hyz Hust Github First, we propose visuriddles, a benchmark for avr, featuring tasks meticulously constructed to assess models' reasoning capacities across five core dimensions and two high level reasoning categories. Our extensive experimental results on visuriddles empirically validate that fine grained visual perception is the principal bottleneck and our synthesis framework markedly enhances the performance of contemporary mllms on these challenging tasks. our code and dataset will be released at \url { github yh hust visuriddles}.