Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Chen, Qiguang"'
Large Language Model (LLM)-based agents exhibit significant potential across various domains, operating as interactive systems that process environmental observations to generate executable actions for target tasks. The effectiveness of these agents
Externí odkaz:
http://arxiv.org/abs/2408.09559
Autor:
Chen, Qiguang, Pan, Ya-Jun
Industry 4.0 proposes the integration of artificial intelligence (AI) into manufacturing and other industries to create smart collaborative systems which enhance efficiency. The aim of this paper is to develop a flexible and adaptive framework to gen
Externí odkaz:
http://arxiv.org/abs/2407.08534
Cross-lingual chain-of-thought can effectively complete reasoning tasks across languages, which gains increasing attention. Recently, dominant approaches in the literature improve cross-lingual alignment capabilities by integrating reasoning knowledg
Externí odkaz:
http://arxiv.org/abs/2406.13940
Autor:
Qin, Libo, Wei, Fuxuan, Chen, Qiguang, Zhou, Jingxuan, Huang, Shijue, Si, Jiasheng, Lu, Wenpeng, Che, Wanxiang
Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Neverthe
Externí odkaz:
http://arxiv.org/abs/2406.10505
Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which gains increasing attention. Nevertheless, the current MCoT benchmark still faces some challenges: (1)
Externí odkaz:
http://arxiv.org/abs/2405.16473
Autor:
Qin, Libo, Chen, Qiguang, Feng, Xiachong, Wu, Yang, Zhang, Yongheng, Li, Yinghui, Li, Min, Che, Wanxiang, Yu, Philip S.
While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored. This study aims to address this gap
Externí odkaz:
http://arxiv.org/abs/2405.12819
Autor:
Qin, Libo, Chen, Qiguang, Zhou, Yuhang, Chen, Zhi, Li, Yinghui, Liao, Lizi, Li, Min, Che, Wanxiang, Yu, Philip S.
Multilingual Large Language Models are capable of using powerful Large Language Models to handle and respond to queries in multiple languages, which achieves remarkable success in multilingual natural language processing tasks. Despite these breakthr
Externí odkaz:
http://arxiv.org/abs/2404.04925
Autor:
Qin, Libo, Pan, Wenbo, Chen, Qiguang, Liao, Lizi, Yu, Zhou, Zhang, Yue, Che, Wanxiang, Li, Min
End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trai
Externí odkaz:
http://arxiv.org/abs/2311.09008
Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention. Specifically, zero-shot CoT achieves remarkable improvements in a wide range of reaso
Externí odkaz:
http://arxiv.org/abs/2310.14799
Autor:
Hu, Mengkang, Mu, Yao, Yu, Xinmiao, Ding, Mingyu, Wu, Shiguang, Shao, Wenqi, Chen, Qiguang, Wang, Bin, Qiao, Yu, Luo, Ping
This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations. Recently, prompting Large Language Models (LLM
Externí odkaz:
http://arxiv.org/abs/2310.08582