Full Model
"Full Model" research encompasses the development and improvement of large-scale machine learning models across diverse applications, aiming to enhance performance, efficiency, and robustness. Current research focuses on addressing model vulnerabilities (e.g., adversarial attacks, hallucinations), improving efficiency for resource-constrained devices, and developing specialized models for specific domains (e.g., finance, astronomy, medical imaging). This work is significant for advancing AI capabilities in various fields and for mitigating potential risks associated with deploying complex models in real-world settings.
Papers
Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination
Salman Rakin, Md. A.R. Shibly, Zahin M. Hossain, Zeeshan Khan, Md. Mostofa Akbar
Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models
Nils Blank, Moritz Reuss, Marcel Rühle, Ömer Erdinç Yağmurlu, Fabian Wenzel, Oier Mees, Rudolf Lioutikov
From Attention to Activation: Unravelling the Enigmas of Large Language Models
Prannay Kaul, Chengcheng Ma, Ismail Elezi, Jiankang Deng
Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models
Zhijie Tan, Xu Chu, Weiping Li, Tong Mo
Semantic-guided Search for Efficient Program Repair with Large Language Models
Thanh Le-Cong, Bach Le, Toby Murray
Comparative Study of Multilingual Idioms and Similes in Large Language Models
Paria Khoshtab, Danial Namazifard, Mostafa Masoudi, Ali Akhgary, Samin Mahdizadeh Sani, Yadollah Yaghoobzadeh
Promoting cross-modal representations to improve multimodal foundation models for physiological signals
Ching Fang, Christopher Sandino, Behrooz Mahasseni, Juri Minxha, Hadi Pouransari, Erdrin Azemi, Ali Moin, Ellen Zippi
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
Giannis Daras, Weili Nie, Karsten Kreis, Alex Dimakis, Morteza Mardani, Nikola Borislavov Kovachki, Arash Vahdat
Analyzing Closed-loop Training Techniques for Realistic Traffic Agent Models in Autonomous Highway Driving Simulations
Matthias Bitzer, Reinis Cimurs, Benjamin Coors, Johannes Goth, Sebastian Ziesche, Philipp Geiger, Maximilian Naumann
Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model
Xinyu Yuan, Yan Qiao, Pei Zhao, Rongyao Hu, Benchu Zhang
Residual vector quantization for KV cache compression in large language model
Ankur Kumar
Students Rather Than Experts: A New AI For Education Pipeline To Model More Human-Like And Personalised Early Adolescences
Yiping Ma, Shiyu Hu, Xuchen Li, Yipei Wang, Shiqing Liu, Kang Hao Cheong
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models
Ahmed Elbakary, Chaouki Ben Issaid, Tamer ElBatt, Karim Seddik, Mehdi Bennis
MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures
Aizan Zafar, Kshitij Mishra, Asif Ekbal
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model
Haoye Chai, Shiyuan Zhang, Xiaoqian Qi, Yong Li
Physically Guided Deep Unsupervised Inversion for 1D Magnetotelluric Models
Paul Goyes-Peñafiel, Umair bin Waheed, Henry Arguello
Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging
Mingxin Li, Zhijie Nie, Yanzhao Zhang, Dingkun Long, Richong Zhang, Pengjun Xie
Making Every Frame Matter: Continuous Video Understanding for Large Models via Adaptive State Modeling
Hao Wu, Donglin Bai, Shiqi Jiang, Qianxi Zhang, Yifan Yang, Ting Cao, Fengyuan Xu
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model
Tianqianjin Lin, Pengwei Yan, Kaisong Song, Zhuoren Jiang, Yangyang Kang, Jun Lin, Weikang Yuan, Junjie Cao, Changlong Sun, Xiaozhong Liu