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pro vyhledávání: '"Su, Zhaochen"'
Large Vision-Language Models (LVLMs) have become pivotal at the intersection of computer vision and natural language processing. However, the full potential of LVLMs Retrieval-Augmented Generation (RAG) capabilities remains underutilized. Existing wo
Externí odkaz:
http://arxiv.org/abs/2409.14083
Autor:
Su, Zhaochen, Zhang, Jun, Qu, Xiaoye, Zhu, Tong, Li, Yanshu, Sun, Jiashuo, Li, Juntao, Zhang, Min, Cheng, Yu
Large language models (LLMs) have achieved impressive advancements across numerous disciplines, yet the critical issue of knowledge conflicts, a major source of hallucinations, has rarely been studied. Only a few research explored the conflicts betwe
Externí odkaz:
http://arxiv.org/abs/2408.12076
Reasoning about time is essential for Large Language Models (LLMs) to understand the world. Previous works focus on solving specific tasks, primarily on time-sensitive question answering. While these methods have proven effective, they cannot general
Externí odkaz:
http://arxiv.org/abs/2406.14192
Autor:
Su, Zhaochen, Li, Juntao, Zhang, Jun, Zhu, Tong, Qu, Xiaoye, Zhou, Pan, Bowen, Yan, Cheng, Yu, zhang, Min
Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristic
Externí odkaz:
http://arxiv.org/abs/2406.09072
Recent research has revealed that neural language models at scale suffer from poor temporal generalization capability, i.e., the language model pre-trained on static data from past years performs worse over time on emerging data. Existing methods mai
Externí odkaz:
http://arxiv.org/abs/2210.17127
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Akademický článek
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Publikováno v:
Chinese Chemical Letters; April 2022, Vol. 33 Issue: 4 p2096-2100, 5p
Publikováno v:
Chinese Chemical Letters; May 2023, Vol. 34 Issue: 5