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of 21
pro vyhledávání: '"Yuan, Chenhan"'
Transformer-based large language models (LLMs) exhibit limitations such as generating unsafe responses, unreliable reasoning, etc. Existing inference intervention approaches attempt to mitigate these issues by finetuning additional models to produce
Externí odkaz:
http://arxiv.org/abs/2408.10764
Recent advancements in Large Language Models (LLMs) have demonstrated their potential in delivering accurate answers to questions about world knowledge. Despite this, existing benchmarks for evaluating LLMs in healthcare predominantly focus on medica
Externí odkaz:
http://arxiv.org/abs/2406.11328
Autor:
Hu, Gang, Qin, Ke, Yuan, Chenhan, Peng, Min, Lopez-Lira, Alejandro, Wang, Benyou, Ananiadou, Sophia, Huang, Jimin, Xie, Qianqian
While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity. To bridge t
Externí odkaz:
http://arxiv.org/abs/2403.06249
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:
Yuan, Chenhan, Eldardiry, Hoda
Temporal knowledge graphs (TKGs) have shown promise for reasoning tasks by incorporating a temporal dimension to represent how facts evolve over time. However, existing TKG reasoning (TKGR) models lack explainability due to their black-box nature. Re
Externí odkaz:
http://arxiv.org/abs/2310.04889
Temporal reasoning is a crucial NLP task, providing a nuanced understanding of time-sensitive contexts within textual data. Although recent advancements in LLMs have demonstrated their potential in temporal reasoning, the predominant focus has been o
Externí odkaz:
http://arxiv.org/abs/2310.01074
The goal of temporal relation extraction is to infer the temporal relation between two events in the document. Supervised models are dominant in this task. In this work, we investigate ChatGPT's ability on zero-shot temporal relation extraction. We d
Externí odkaz:
http://arxiv.org/abs/2304.05454
Autor:
Yuan, Chenhan
Relation extraction is the problem of extracting relations between entities described in the text. Relations identify a common "fact" described by distinct entities. Conventional relation extraction approaches focus on supervised binary intra-sentenc
Externí odkaz:
http://hdl.handle.net/10919/112572
Publikováno v:
In Knowledge-Based Systems 28 February 2024 286