In a new study co-authored by Apple researchers , an open-source large language model (LLM) saw big performance improvements after being told to check its own work by using one simple productivity trick. Here are the details.
After an LLM is trained, its quality is usually refined further through a post-training step known as reinforcement learning from human feedback (RLHF).
With RLHF, every time a model gives an answer, human labelers can either give it a thumbs up, which
Musk said on social media platform X.com over the weekend that Grok 2.5, touted as the company’s “best model last year”
Elon Musk 日前在 X 宣布，旗下 AI 企業 xAI 已將聊天機械人 Grok 2.5 模型開源，並透露 Grok 3 將於約 6 個月後同樣開源。Musk 形容 Grok 2.5 為「2024 年最佳模型」，此舉將進一步推動 AI 技術開放共享。
Grok 是 Elon Musk 旗下 AI 企業 xAI 開發的聊天機
Apple researchers have developed an adapted version of the SlowFast-LLaVA model that beats larger models at long-form video analysis and understanding. Here’s what that means.
Very basically, when an LLM is trained to also understand video, it learns to split videos into frames, apply computer vision to extract visual features, analyze how those features change over time, and align all of that with language so it can describe or reason about the video in the form of text.
One very inefficient way to do this is