Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Yang, Kailai"'
The emergence of social media has made the spread of misinformation easier. In the financial domain, the accuracy of information is crucial for various aspects of financial market, which has made financial misinformation detection (FMD) an urgent pro
Externí odkaz:
http://arxiv.org/abs/2409.16452
Recent advancements in large language model alignment leverage token-level supervisions to perform fine-grained preference optimization. However, existing token-level alignment methods either optimize on all available tokens, which can be noisy and i
Externí odkaz:
http://arxiv.org/abs/2408.13518
Autor:
Liu, Zhiwei, Yang, Kailai, Xie, Qianqian, de Kock, Christine, Ananiadou, Sophia, Hovy, Eduard
Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society. However, current methods for cross-domain misinformation detection rely on time and resources consuming fine-tuning and comp
Externí odkaz:
http://arxiv.org/abs/2406.11093
Recent advancements in large language models (LLMs) focus on aligning to heterogeneous human expectations and values via multi-objective preference alignment. However, existing methods are dependent on the policy model parameters, which require high-
Externí odkaz:
http://arxiv.org/abs/2403.17141
The internet has brought both benefits and harms to society. A prime example of the latter is misinformation, including conspiracy theories, which flood the web. Recent advances in natural language processing, particularly the emergence of large lang
Externí odkaz:
http://arxiv.org/abs/2403.06765
Autor:
Xiao, Mengxi, Xie, Qianqian, Kuang, Ziyan, Liu, Zhicheng, Yang, Kailai, Peng, Min, Han, Weiguang, Huang, Jimin
Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity. Previous LLMs in co
Externí odkaz:
http://arxiv.org/abs/2403.05574
Autor:
Xie, Qianqian, Han, Weiguang, Chen, Zhengyu, Xiang, Ruoyu, Zhang, Xiao, He, Yueru, Xiao, Mengxi, Li, Dong, Dai, Yongfu, Feng, Duanyu, Xu, Yijing, Kang, Haoqiang, Kuang, Ziyan, Yuan, Chenhan, Yang, Kailai, Luo, Zheheng, Zhang, Tianlin, Liu, Zhiwei, Xiong, Guojun, Deng, Zhiyang, Jiang, Yuechen, Yao, Zhiyuan, Li, Haohang, Yu, Yangyang, Hu, Gang, Huang, Jiajia, Liu, Xiao-Yang, Lopez-Lira, Alejandro, Wang, Benyou, Lai, Yanzhao, Wang, Hao, Peng, Min, Ananiadou, Sophia, Huang, Jimin
LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid development of LLMs, and the complexity of financial tasks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2402.12659
Sentiment analysis and emotion detection are important research topics in natural language processing (NLP) and benefit many downstream tasks. With the widespread application of LLMs, researchers have started exploring the application of LLMs based o
Externí odkaz:
http://arxiv.org/abs/2401.08508
Autor:
Hua, Yining, Liu, Fenglin, Yang, Kailai, Li, Zehan, Na, Hongbin, Sheu, Yi-han, Zhou, Peilin, Moran, Lauren V., Ananiadou, Sophia, Beam, Andrew, Torous, John
The integration of large language models (LLMs) in mental health care is an emerging field. There is a need to systematically review the application outcomes and delineate the advantages and limitations in clinical settings. This review aims to provi
Externí odkaz:
http://arxiv.org/abs/2401.02984
Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications. This paper offers a perspective on using LLMs in mental health applications. It discusses the instabi
Externí odkaz:
http://arxiv.org/abs/2311.11267