Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Han, Weiguang"'
Autor:
Xie, Qianqian, Li, Dong, Xiao, Mengxi, Jiang, Zihao, Xiang, Ruoyu, Zhang, Xiao, Chen, Zhengyu, He, Yueru, Han, Weiguang, Yang, Yuzhe, Chen, Shunian, Zhang, Yifei, Shen, Lihang, Kim, Daniel, Liu, Zhiwei, Luo, Zheheng, Yu, Yangyang, Cao, Yupeng, Deng, Zhiyang, Yao, Zhiyuan, Li, Haohang, Feng, Duanyu, Dai, Yongfu, Somasundaram, VijayaSai, Lu, Peng, Zhao, Yilun, Long, Yitao, Xiong, Guojun, Smith, Kaleb, Yu, Honghai, Lai, Yanzhao, Peng, Min, Nie, Jianyun, Suchow, Jordan W., Liu, Xiao-Yang, Wang, Benyou, Lopez-Lira, Alejandro, Huang, Jimin, Ananiadou, Sophia
Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce \t
Externí odkaz:
http://arxiv.org/abs/2408.11878
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
D\'olares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English
Autor:
Zhang, Xiao, Xiang, Ruoyu, Yuan, Chenhan, Feng, Duanyu, Han, Weiguang, Lopez-Lira, Alejandro, Liu, Xiao-Yang, Ananiadou, Sophia, Peng, Min, Huang, Jimin, Xie, Qianqian
Despite Spanish's pivotal role in the global finance industry, a pronounced gap exists in Spanish financial natural language processing (NLP) and application studies compared to English, especially in the era of large language models (LLMs). To bridg
Externí odkaz:
http://arxiv.org/abs/2402.07405
Autor:
Dai, Yongfu, Feng, Duanyu, Huang, Jimin, Jia, Haochen, Xie, Qianqian, Zhang, Yifang, Han, Weiguang, Tian, Wei, Wang, Hao
General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI. However, the current evaluations of these LLMs in LegalAI are defined by the experts of computer science, lacking consistency with the logic of legal prac
Externí odkaz:
http://arxiv.org/abs/2310.05620
Autor:
Feng, Duanyu, Dai, Yongfu, Huang, Jimin, Zhang, Yifang, Xie, Qianqian, Han, Weiguang, Chen, Zhengyu, Lopez-Lira, Alejandro, Wang, Hao
In the financial industry, credit scoring is a fundamental element, shaping access to credit and determining the terms of loans for individuals and businesses alike. Traditional credit scoring methods, however, often grapple with challenges such as n
Externí odkaz:
http://arxiv.org/abs/2310.00566
Autor:
Xie, Qianqian, Han, Weiguang, Zhang, Xiao, Lai, Yanzhao, Peng, Min, Lopez-Lira, Alejandro, Huang, Jimin
Although large language models (LLMs) has shown great performance on natural language processing (NLP) in the financial domain, there are no publicly available financial tailtored LLMs, instruction tuning datasets, and evaluation benchmarks, which is
Externí odkaz:
http://arxiv.org/abs/2306.05443
Recently, large language models (LLMs) like ChatGPT have demonstrated remarkable performance across a variety of natural language processing tasks. However, their effectiveness in the financial domain, specifically in predicting stock market movement
Externí odkaz:
http://arxiv.org/abs/2304.05351
Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pair trading as human expertise. It requires RL methods to make thousand
Externí odkaz:
http://arxiv.org/abs/2304.00364
Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However,
Externí odkaz:
http://arxiv.org/abs/2301.10724