no code implementations • 16 May 2024 • Jian Chen, Peilin Zhou, Yining Hua, Yingxin Loh, Kehui Chen, Ziyuan Li, Bing Zhu, Junwei Liang
Accurate evaluation of financial question answering (QA) systems necessitates a comprehensive dataset encompassing diverse question types and contexts.
no code implementations • 25 Apr 2024 • Andrew Liu, Hongjian Zhou, Yining Hua, Omid Rohanian, Lei Clifton, David A. Clifton
We conduct a detailed evaluation of the existing sixteen LLMs in healthcare under both zero-shot and few-shot (i. e., 1, 3, 5-shot) learning settings.
no code implementations • 1 Jan 2024 • Yining Hua, Fenglin Liu, Kailai Yang, Zehan Li, Yi-han Sheu, Peilin Zhou, Lauren V. Moran, Sophia Ananiadou, Andrew Beam
Objective: The growing use of large language models (LLMs) stimulates a need for a comprehensive review of their applications and outcomes in mental health care contexts.
1 code implementation • 9 Nov 2023 • Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S. Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Chenyu You, Xian Wu, Yefeng Zheng, Lei Clifton, Zheng Li, Jiebo Luo, David A. Clifton
Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face.
no code implementations • 7 Nov 2023 • Peilin Zhou, Meng Cao, You-Liang Huang, Qichen Ye, Peiyan Zhang, Junling Liu, Yueqi Xie, Yining Hua, Jaeboum Kim
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visual assistance remain unexplored.
no code implementations • 1 Nov 2023 • Zhen Guo, Yining Hua
This work demonstrates a method using continuous training and instruction fine-tuning to rapidly adapt Llama 2 base models to the Chinese medical domain.
1 code implementation • 27 Oct 2023 • Junling Liu, ZiMing Wang, Qichen Ye, Dading Chong, Peilin Zhou, Yining Hua
This method enhances the model's ability to generate medical captions and answer complex medical queries.
1 code implementation • 13 Oct 2023 • Qichen Ye, Junling Liu, Dading Chong, Peilin Zhou, Yining Hua, Fenglin Liu, Meng Cao, ZiMing Wang, Xuxin Cheng, Zhu Lei, Zhenhua Guo
In the CPT and SFT phases, Qilin-Med achieved 38. 4% and 40. 0% accuracy on the CMExam test set, respectively.
2 code implementations • 28 Jun 2023 • Yining Hua, Jiageng Wu, Shixu Lin, Minghui Li, Yujie Zhang, Dinah Foer, Siwen Wang, Peilin Zhou, Jie Yang, Li Zhou
Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data.
1 code implementation • 5 Jun 2023 • Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, Michael Lingzhi Li
To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations.
1 code implementation • 28 Feb 2023 • Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jaeboum Kim, Fangzhao Wu, Sunghun Kim
To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method.
1 code implementation • 23 Feb 2023 • Jiageng Wu, Xian Wu, Yining Hua, Shixu Lin, Yefeng Zheng, Jie Yang
Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
1 code implementation • 13 Nov 2022 • Qingcheng Zeng, Lucas Garay, Peilin Zhou, Dading Chong, Yining Hua, Jiageng Wu, Yikang Pan, Han Zhou, Rob Voigt, Jie Yang
Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world's languages.
1 code implementation • 10 Nov 2022 • Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jae Boum Kim, Shoujin Wang, Sunghun Kim
Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e. g., item substitution) and insensitive to mild augmentations (e. g., featurelevel dropout masking).
1 code implementation • 28 Sep 2022 • Peilin Zhou, Zeqiang Wang, Dading Chong, Zhijiang Guo, Yining Hua, Zichang Su, Zhiyang Teng, Jiageng Wu, Jie Yang
To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9, 101 entities (in 5, 278 tweets).
1 code implementation • 29 Jun 2022 • Yining Hua, Hang Jiang, Shixu Lin, Jie Yang, Joseph M. Plasek, David W. Bates, Li Zhou
Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges.
1 code implementation • LREC 2022 • Hang Jiang, Yining Hua, Doug Beeferman, Deb Roy
We release the dataset and make both the Stanza pipeline and BERTweet-based models available "off-the-shelf" for use in future Tweet NLP research.
Ranked #3 on Dependency Parsing on Tweebank