Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Yu, Bihui"'
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capability has become a crucial prerequisite. Consequently, managing and understanding large-scale video datasets has gained increas
Externí odkaz:
http://arxiv.org/abs/2407.01937
Autor:
Tan, Cheng, Wei, Jingxuan, Sun, Linzhuang, Gao, Zhangyang, Li, Siyuan, Yu, Bihui, Guo, Ruifeng, Li, Stan Z.
Large language models equipped with retrieval-augmented generation (RAG) represent a burgeoning field aimed at enhancing answering capabilities by leveraging external knowledge bases. Although the application of RAG with language-only models has been
Externí odkaz:
http://arxiv.org/abs/2405.20834
Knowledge distillation, transferring knowledge from a teacher model to a student model, has emerged as a powerful technique in neural machine translation for compressing models or simplifying training targets. Knowledge distillation encompasses two p
Externí odkaz:
http://arxiv.org/abs/2404.14827
In the fields of computer vision and natural language processing, multimodal chart question-answering, especially involving color, structure, and textless charts, poses significant challenges. Traditional methods, which typically involve either direc
Externí odkaz:
http://arxiv.org/abs/2404.01548
Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory
Having the ability to empathize is crucial for accurately representing human behavior during conversations. Despite numerous research aim to improve the cognitive capability of models by incorporating external knowledge, there has been limited attent
Externí odkaz:
http://arxiv.org/abs/2312.08702
Knowledge distillation, a technique for model compression and performance enhancement, has gained significant traction in Neural Machine Translation (NMT). However, existing research primarily focuses on empirical applications, and there is a lack of
Externí odkaz:
http://arxiv.org/abs/2312.08585
The continuous development of artificial intelligence has a profound impact on biomedicine and other fields, providing new research ideas and technical methods. Brain-inspired computing is an important intersection between multimodal technology and b
Externí odkaz:
http://arxiv.org/abs/2312.07213
Autor:
Tan, Cheng, Wei, Jingxuan, Gao, Zhangyang, Sun, Linzhuang, Li, Siyuan, Guo, Ruifeng, Yu, Bihui, Li, Stan Z.
Multimodal reasoning is a challenging task that requires models to reason across multiple modalities to answer questions. Existing approaches have made progress by incorporating language and visual modalities into a two-stage reasoning framework, sep
Externí odkaz:
http://arxiv.org/abs/2311.14109
Autor:
Guo, Ruifeng, Wei, Jingxuan, Sun, Linzhuang, Yu, Bihui, Chang, Guiyong, Liu, Dawei, Zhang, Sibo, Yao, Zhengbing, Xu, Mingjun, Bu, Liping
With the significant advancements of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), the development of image-text multimodal models has garnered widespread attention. Current surveys on image-text multimodal models ma
Externí odkaz:
http://arxiv.org/abs/2309.15857
Autor:
Wei, Jingxuan, Tan, Cheng, Gao, Zhangyang, Sun, Linzhuang, Li, Siyuan, Yu, Bihui, Guo, Ruifeng, Li, Stan Z.
Multimodal reasoning is a critical component in the pursuit of artificial intelligence systems that exhibit human-like intelligence, especially when tackling complex tasks. While the chain-of-thought (CoT) technique has gained considerable attention,
Externí odkaz:
http://arxiv.org/abs/2307.12626