Zobrazeno 1 - 10
of 4 412
pro vyhledávání: '"CAI, Jie"'
Autor:
Zhao, Ranchi, Thai, Zhen Leng, Zhang, Yifan, Hu, Shengding, Ba, Yunqi, Zhou, Jie, Cai, Jie, Liu, Zhiyuan, Sun, Maosong
Publikováno v:
EMNLP 2024
The performance of Large Language Models (LLMs) is substantially influenced by the pretraining corpus, which consists of vast quantities of unsupervised data processed by the models. Despite its critical role in model performance, ensuring the qualit
Externí odkaz:
http://arxiv.org/abs/2410.05639
In recent decades, there has been substantial advances in time series models and benchmarks across various individual tasks, such as time series forecasting, classification, and anomaly detection. Meanwhile, compositional reasoning in time series is
Externí odkaz:
http://arxiv.org/abs/2410.04047
Autor:
Yao, Yuan, Yu, Tianyu, Zhang, Ao, Wang, Chongyi, Cui, Junbo, Zhu, Hongji, Cai, Tianchi, Li, Haoyu, Zhao, Weilin, He, Zhihui, Chen, Qianyu, Zhou, Huarong, Zou, Zhensheng, Zhang, Haoye, Hu, Shengding, Zheng, Zhi, Zhou, Jie, Cai, Jie, Han, Xu, Zeng, Guoyang, Li, Dahai, Liu, Zhiyuan, Sun, Maosong
The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLM
Externí odkaz:
http://arxiv.org/abs/2408.01800
The LLM Agent, equipped with a code interpreter, is capable of automatically solving real-world coding tasks, such as data analysis and image editing. However, existing benchmarks primarily focus on either simplistic tasks, such as completing a few l
Externí odkaz:
http://arxiv.org/abs/2407.16732
Autor:
Zhang, He, Wu, Chuhao, Xie, Jingyi, Rubino, Fiona, Graver, Sydney, Kim, ChanMin, Carroll, John M., Cai, Jie
Qualitative research, renowned for its in-depth exploration of complex phenomena, often involves time-intensive analysis, particularly during the coding stage. Existing software for qualitative evaluation frequently lacks automatic coding capabilitie
Externí odkaz:
http://arxiv.org/abs/2407.14925
As Large-Scale Language Models (LLMs) continue to evolve, they demonstrate significant enhancements in performance and an expansion of functionalities, impacting various domains, including education. In this study, we conducted interviews with 14 stu
Externí odkaz:
http://arxiv.org/abs/2407.12723
Identity work in Human-Computer Interaction (HCI) has focused on the marginalized group to explore designs to support their asset (what they have). However, little has been explored specifically on the identity work of people with disabilities, speci
Externí odkaz:
http://arxiv.org/abs/2404.14305
Autor:
Hu, Shengding, Tu, Yuge, Han, Xu, He, Chaoqun, Cui, Ganqu, Long, Xiang, Zheng, Zhi, Fang, Yewei, Huang, Yuxiang, Zhao, Weilin, Zhang, Xinrong, Thai, Zheng Leng, Zhang, Kaihuo, Wang, Chongyi, Yao, Yuan, Zhao, Chenyang, Zhou, Jie, Cai, Jie, Zhai, Zhongwu, Ding, Ning, Jia, Chao, Zeng, Guoyang, Li, Dahai, Liu, Zhiyuan, Sun, Maosong
The burgeoning interest in developing Large Language Models (LLMs) with up to trillion parameters has been met with concerns regarding resource efficiency and practical expense, particularly given the immense cost of experimentation. This scenario un
Externí odkaz:
http://arxiv.org/abs/2404.06395
Content creators increasingly utilize generative artificial intelligence (Gen-AI) on platforms such as YouTube, TikTok, Instagram, and various blogging sites to produce imaginative images, AI-generated videos, and articles using Large Language Models
Externí odkaz:
http://arxiv.org/abs/2403.06039
Social media users may perceive moderation decisions by the platform differently, which can lead to frustration and dropout. This study investigates users' perceived justice and fairness of online moderation decisions when they are exposed to various
Externí odkaz:
http://arxiv.org/abs/2403.06034