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ThoT is a novel prompting strategy designed to revolutionize how language models handle chaotic contexts, enabling them to manage complex, interleaved information more effectively. LLaVA-o1, developed by researchers at Peking University, is the first visual language model capable of spontaneous, systematic reasoning. Unlike traditional chain-of-thought prompting, LLaVA-o1 engages in autonomous multistage reasoning, including summarization, visual interpretation, logical reasoning, and conclusion generation. The model is efficient with small data, having been trained on just 100,000 samples. The research was conducted by PKU1898, with resources available on Github and in a published paper.
Introducing LLaVA-o1: The first visual language model capable of spontaneous, systematic reasoning, similar to GPT-o1! https://t.co/rWM1NoTpgE
LLaVA-o1 🔥 NEW visual language model with spontaneous and systematic reasoning, like GPT-o1! By @PKU1898 Github:https://t.co/vMMN7yvAwe Paper:https://t.co/Oh1MhilDoF ✨ Autonomous Multistage Reasoning ✨ Efficient with Small Data: Trained on 100k samples ✨ Innovative…
LLaVA-o1, a novel VLM designed to conduct autonomous multistage reasoning. Unlike chain-of-thought prompting, LLaVA-o1 independently engages in sequential stages of summarization, visual interpretation, logical reasoning, and conclusion generation. https://t.co/CgIWDpNvma
