Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Feng, Duanyu"'
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:
Wang, Yuxin, Feng, Duanyu, Dai, Yongfu, Chen, Zhengyu, Huang, Jimin, Ananiadou, Sophia, Xie, Qianqian, Wang, Hao
Data serves as the fundamental foundation for advancing deep learning, particularly tabular data presented in a structured format, which is highly conducive to modeling. However, even in the era of LLM, obtaining tabular data from sensitive domains r
Externí odkaz:
http://arxiv.org/abs/2408.02927
The success of the reward model in distinguishing between responses with subtle safety differences depends critically on the high-quality preference dataset, which should capture the fine-grained nuances of harmful and harmless responses. This motiva
Externí odkaz:
http://arxiv.org/abs/2406.08124
Autor:
Huang, Youcheng, Tang, Jingkun, Feng, Duanyu, Zhang, Zheng, Lei, Wenqiang, Lv, Jiancheng, Cohn, Anthony G.
People tell lies when seeking rewards. Large language models (LLMs) are aligned to human values with reinforcement learning where they get rewards if they satisfy human preference. We find that this also induces dishonesty in helpful and harmless ali
Externí odkaz:
http://arxiv.org/abs/2406.01931
Reinforcement Learning from Human Feedback (RLHF) is a widely used framework for the training of language models. However, the process of using RLHF to develop a language model that is well-aligned presents challenges, especially when it comes to opt
Externí odkaz:
http://arxiv.org/abs/2404.04932
Direct Preference Optimization (DPO), which derives reward signals directly from pairwise preference data, has shown its effectiveness on aligning Large Language Models (LLMs) with human preferences. Despite its widespread use across various tasks, D
Externí odkaz:
http://arxiv.org/abs/2404.04626
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
Deploying dense retrieval models efficiently is becoming increasingly important across various industries. This is especially true for enterprise search services, where customizing search engines to meet the time demands of different enterprises in d
Externí odkaz:
http://arxiv.org/abs/2401.12540
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