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pro vyhledávání: '"He, Hongliang"'
Autor:
He, Hongliang, Yao, Wenlin, Ma, Kaixin, Yu, Wenhao, Zhang, Hongming, Fang, Tianqing, Lan, Zhenzhong, Yu, Dong
The rapid development of large language and multimodal models has sparked significant interest in using proprietary models, such as GPT-4o, to develop autonomous agents capable of handling real-world scenarios like web navigation. Although recent ope
Externí odkaz:
http://arxiv.org/abs/2410.19609
Large multimodal foundation models have been extensively utilized for image recognition tasks guided by instructions, yet there remains a scarcity of domain expertise in industrial vibration signal analysis. This paper presents a pipeline named VSLLa
Externí odkaz:
http://arxiv.org/abs/2409.07482
Publikováno v:
Diabetes [Diabetes] 2023 Jul 01; Vol. 72 (7), pp. 1038.
Pornographic content occurring in human-machine interaction dialogues can cause severe side effects for users in open-domain dialogue systems. However, research on detecting pornographic language within human-machine interaction dialogues is an impor
Externí odkaz:
http://arxiv.org/abs/2403.13250
Autor:
He, Hongliang, Yao, Wenlin, Ma, Kaixin, Yu, Wenhao, Dai, Yong, Zhang, Hongming, Lan, Zhenzhong, Yu, Dong
The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, which drives innovation in creating advanced web agents. Existing web agents typically only handl
Externí odkaz:
http://arxiv.org/abs/2401.13919
Autor:
Zhang, Junlei, He, Hongliang, Song, Nirui, Zhou, Zhanchao, He, Shuyuan, Zhang, Shuai, Qiu, Huachuan, Li, Anqi, Dai, Yong, Ma, Lizhi, Lan, Zhenzhong
The critical field of psychology necessitates a comprehensive benchmark to enhance the evaluation and development of domain-specific Large Language Models (LLMs). Existing MMLU-type benchmarks, such as C-EVAL and CMMLU, include psychology-related sub
Externí odkaz:
http://arxiv.org/abs/2311.09861
NSFW (Not Safe for Work) content, in the context of a dialogue, can have severe side effects on users in open-domain dialogue systems. However, research on detecting NSFW language, especially sexually explicit content, within a dialogue context has s
Externí odkaz:
http://arxiv.org/abs/2309.09749
Publikováno v:
Zhejiang dianli, Vol 43, Iss 11, Pp 3-14 (2024)
As the capacity of renewable power plants connected to the grid increases year by year， optimizing the control strategies for their converters becomes crucial for enhancing system stability. Currently， the primary control strategies in renewable
Externí odkaz:
https://doaj.org/article/a0b2267c62874412b535cd98f880158a
Dialogue safety remains a pervasive challenge in open-domain human-machine interaction. Existing approaches propose distinctive dialogue safety taxonomies and datasets for detecting explicitly harmful responses. However, these taxonomies may not be s
Externí odkaz:
http://arxiv.org/abs/2307.16457
Considerable research efforts have been devoted to ensuring that large language models (LLMs) align with human values and generate safe text. However, an excessive focus on sensitivity to certain topics can compromise the model's robustness in follow
Externí odkaz:
http://arxiv.org/abs/2307.08487