7 posts are grouped under this topic.
While Diffusion models—the core of modern generative AI—exhibit an extraordinary ability to produce high-quality samples, the issue of information loss during the data reconstruction process remains a significant challen
The current trend in AI modeling is shifting from simply increasing parameter counts to focusing on how to maintain high performance with less data while operating models efficiently. Particularly, as Large Language Mode
One of the most challenging tasks in modern AI agent design is determining how to integrate distributed data from diverse domains and sources into a single, organic workflow. Beyond merely collecting API response data fr
While there is a close relationship between data volume and model performance, unconditional data accumulation does not necessarily guarantee optimal intelligence. Traditionally, acquiring more training data was consider
Today, while AI technology has achieved phenomenal progress through the emergence of Large Language Models (LLMs) like GPT and Claude, it simultaneously faces practical challenges regarding immense computational resource
Today, AI technology has achieved revolutionary progress in understanding and generating human language thanks to the emergence of Large Language Models (LLMs) like GPT and Claude [S2366]. However, while these massive mo
Large Language Models (LLMs), which sit at the heart of recent AI advancements, are revolutionizing various industries by generating human-like text based on vast amounts of data [S2225]. However, when deploying these mo