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pro vyhledávání: '"WANG, Xiaochen"'
Autor:
Wang, Xiaochen, He, Junqing, Chen, Liang, Yang, Reza Haf Zhe, Wang, Yiru, Meng, Xiangdi, Pan, Kunhao, Sui, Zhifang
Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. However, Multi-hop Question Answering (MHQA) remains challenging for many existing models due to issues
Externí odkaz:
http://arxiv.org/abs/2410.17021
Autor:
Wang, Xiaochen, Li, Aming
Direct reciprocity, stemming from repeated interactions among players, is one of the fundamental mechanisms for understanding the evolution of cooperation. However, canonical strategies for the repeated prisoner's dilemma, such as Win-Stay-Lose-Shift
Externí odkaz:
http://arxiv.org/abs/2409.04696
This study introduces the Federated Medical Knowledge Injection (FEDMEKI) platform, a new benchmark designed to address the unique challenges of integrating medical knowledge into foundation models under privacy constraints. By leveraging a cross-sil
Externí odkaz:
http://arxiv.org/abs/2408.09227
Foundation models have demonstrated remarkable capabilities in handling diverse modalities and tasks, outperforming conventional artificial intelligence (AI) approaches that are highly task-specific and modality-reliant. In the medical domain, howeve
Externí odkaz:
http://arxiv.org/abs/2408.10276
Large Language Models (LLMs) with chain-of-thought (COT) prompting have demonstrated impressive abilities on simple nature language inference tasks. However, they tend to perform poorly on Multi-hop Question Answering (MHQA) tasks due to several chal
Externí odkaz:
http://arxiv.org/abs/2407.02964
Autor:
Zhong, Yuan, Wang, Xiaochen, Wang, Jiaqi, Zhang, Xiaokun, Wang, Yaqing, Huai, Mengdi, Xiao, Cao, Ma, Fenglong
Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existing approaches for EHR data generation predominantly rely on state-of-th
Externí odkaz:
http://arxiv.org/abs/2406.13942
Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence patterns reflected by item IDs, and fine-grained preferences represented
Externí odkaz:
http://arxiv.org/abs/2404.12969
Autor:
Meng, Xiangdi, Dai, Damai, Luo, Weiyao, Yang, Zhe, Wu, Shaoxiang, Wang, Xiaochen, Wang, Peiyi, Dong, Qingxiu, Chen, Liang, Sui, Zhifang
Supervised fine-tuning is the most common method to adapt large language models (LLMs) to downstream tasks, but full fine-tuning LLMs requires massive computational resources. Recently, parameter-efficient fine-tuning (PEFT) methods have been widely
Externí odkaz:
http://arxiv.org/abs/2402.16141
Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of
Externí odkaz:
http://arxiv.org/abs/2402.15700
Autor:
Wang, Jiaqi, Luo, Junyu, Ye, Muchao, Wang, Xiaochen, Zhong, Yuan, Chang, Aofei, Huang, Guanjie, Yin, Ziyi, Xiao, Cao, Sun, Jimeng, Ma, Fenglong
The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With
Externí odkaz:
http://arxiv.org/abs/2402.01077