In artificial intelligence, achieving efficiency in neural networks is a paramount challenge for researchers due to its rapid evolution. The quest for methods minimizing computational demands while preserving or enhancing model performance is ongoing. A particularly...

In recent research, the concept of radioactivity in the context of Large Language Models (LLMs) has been discussed,  with particular attention to the detectability of texts created by LLMs. Here, radioactivity refers to the detectable residues left in a model that has...

The challenge of tailoring general-purpose LLMs to specific tasks without extensive retraining or additional data persists even after significant advancements in the field. Adapting LMs for specialized tasks often requires substantial computational resources and...

The quest for clean, usable data for pretraining Large Language Models (LLMs) resembles searching for treasure amidst chaos. While rich with information, the digital realm is cluttered with extraneous content that complicates the extraction of valuable data. This...

The CHiME-8 MMCSG task focuses on the challenge of transcribing conversations recorded using smart glasses equipped with multiple sensors, including microphones, cameras, and inertial measurement units (IMUs). The dataset aims to help researchers to solve problems...

Point clouds serve as a prevalent representation of 3D data, with the extraction of point-wise features being crucial for various tasks related to 3D understanding. While deep learning methods have made significant strides in this domain, they often rely on large and...