Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Guo, Taicheng"'
Autor:
Chen, Xiuying, Wang, Tairan, Guo, Taicheng, Guo, Kehan, Zhou, Juexiao, Li, Haoyang, Zhuge, Mingchen, Schmidhuber, Jürgen, Gao, Xin, Zhang, Xiangliang
Question Answering (QA) effectively evaluates language models' reasoning and knowledge depth. While QA datasets are plentiful in areas like general domain and biomedicine, academic chemistry is less explored. Chemical QA plays a crucial role in both
Externí odkaz:
http://arxiv.org/abs/2407.16931
Autor:
Hong, Sirui, Lin, Yizhang, Liu, Bang, Liu, Bangbang, Wu, Binhao, Li, Danyang, Chen, Jiaqi, Zhang, Jiayi, Wang, Jinlin, Zhang, Li, Zhang, Lingyao, Yang, Min, Zhuge, Mingchen, Guo, Taicheng, Zhou, Tuo, Tao, Wei, Wang, Wenyi, Tang, Xiangru, Lu, Xiangtao, Zheng, Xiawu, Liang, Xinbing, Fei, Yaying, Cheng, Yuheng, Xu, Zongze, Wu, Chenglin
Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness. However, their performance can be compromised in data science scenarios that require real-time data adjustment, expertise in optimization due to complex dependencies
Externí odkaz:
http://arxiv.org/abs/2402.18679
Autor:
Chen, Xiuying, Wang, Tairan, Zhu, Qingqing, Guo, Taicheng, Gao, Shen, Lu, Zhiyong, Gao, Xin, Zhang, Xiangliang
The summarization capabilities of pretrained and large language models (LLMs) have been widely validated in general areas, but their use in scientific corpus, which involves complex sentences and specialized knowledge, has been less assessed. This pa
Externí odkaz:
http://arxiv.org/abs/2402.14359
Autor:
Liang, Zhenwen, Guo, Kehan, Liu, Gang, Guo, Taicheng, Zhou, Yujun, Yang, Tianyu, Jiao, Jiajun, Pi, Renjie, Zhang, Jipeng, Zhang, Xiangliang
The paper introduces SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level. It addresses a critical educational phase often overlooked in existing benchmarks, spanning high school to pre-college levels.
Externí odkaz:
http://arxiv.org/abs/2402.05138
Autor:
Guo, Taicheng, Chen, Xiuying, Wang, Yaqi, Chang, Ruidi, Pei, Shichao, Chawla, Nitesh V., Wiest, Olaf, Zhang, Xiangliang
Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to the impressive planning and reasoning abilities of LLMs, they have been used as autonomous agents to do many tasks automatically. Recently, based on th
Externí odkaz:
http://arxiv.org/abs/2402.01680
Autor:
Guo, Taicheng, Ma, Changsheng, Chen, Xiuying, Nan, Bozhao, Guo, Kehan, Pei, Shichao, Chawla, Nitesh V., Wiest, Olaf, Zhang, Xiangliang
Reaction prediction, a critical task in synthetic chemistry, is to predict the outcome of a reaction based on given reactants. Generative models like Transformer and VAE have typically been employed to predict the reaction product. However, these lik
Externí odkaz:
http://arxiv.org/abs/2310.04674
Autor:
Guo, Taicheng, Guo, Kehan, Nan, Bozhao, Liang, Zhenwen, Guo, Zhichun, Chawla, Nitesh V., Wiest, Olaf, Zhang, Xiangliang
Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the fie
Externí odkaz:
http://arxiv.org/abs/2305.18365
The cold-start problem has been commonly recognized in recommendation systems and studied by following a general idea to leverage the abundant interaction records of warm users to infer the preference of cold users. However, the performance of these
Externí odkaz:
http://arxiv.org/abs/2207.14370
Akademický článek
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Autor:
Guo, Taicheng, Guo, Kehan, Nan, Bozhao, Liang, Zhenwen, Guo, Zhichun, Chawla, Nitesh V., Wiest, Olaf, Zhang, Xiangliang
Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been rapidly applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c755fc64dc384987394880f194910500