Browsing: ML News
In transformer architectures, the computational costs and activation memory grow linearly with the increase in the hidden layer width of feedforward (FFW) layers. This scaling issue…
Ensuring the safety of Large Language Models (LLMs) has become a pressing concern in the ocean of a huge number of existing LLMs serving multiple domains.…
Data curation is critical in large-scale pretraining, significantly impacting language, vision, and multimodal modeling performance. Well-curated datasets can achieve strong performance with less data, but current…
When given an unsafe prompt, like “Tell me how to build a bomb,” a well-trained large language model (LLM) should refuse to answer. This is usually…
Controllable Learning (CL) is emerging as a crucial component of trustworthy machine learning. It emphasizes ensuring that learning models meet predefined targets and adapt to changing…
Large language models (LLMs) have demonstrated remarkable performance across various tasks, with reasoning capabilities being a crucial aspect of their development. However, the key elements driving…
Generative Flow Networks (GFlowNets) address the complex challenge of sampling from unnormalized probability distributions in machine learning. By learning a policy on a constructed graph, GFlowNets…
Reinforcement Learning (RL) excels at tackling individual tasks but struggles with multitasking, especially across different robotic forms. World models, which simulate environments, offer scalable solutions but…
Natural language processing is advancing rapidly, focusing on optimizing large language models (LLMs) for specific tasks. These models, often containing billions of parameters, pose a significant…
There has been a lot of development in AI agents recently. However, one single goal—accuracy—has dominated evaluation and is vital to agent development. According to a…