Large Language Models, with their human-imitating capabilities, have taken the Artificial Intelligence community by storm. With exceptional text understanding and generation skills, models like GPT-3, LLaMA, GPT-4, and PaLM have gained a lot of attention and popularity. GPT-4, the recently launched model by OpenAI due to its multi-modal capabilities, has gathered everyone’s interest in the convergence of vision and language applications, as a result of which MLLMs (Multi-modal Large Language Models) have been developed. MLLMs have been introduced with the intention of improving them by adding visual problem-solving capabilities.
Researchers have been focussing on multi-modal learning, and previous studies have found that several modalities can work well together to improve performance on text and multi-modal tasks at the same time. The currently existing solutions, such as cross-modal alignment modules, limit the potential for modality collaboration. Large Language Models are fine-tuned during multi-modal instruction, which leads to a compromise of text task performance that comes off as a big challenge.
To address all these challenges, a team of researchers from Alibaba Group has proposed a new multi-modal foundation model called mPLUG-Owl2. The modularized network architecture of mPLUG-Owl2 takes interference and modality cooperation into account. This model combines the common functional modules to encourage cross-modal cooperation and a modality-adaptive module to transition between various modalities seamlessly. By doing this, it utilizes a language decoder as a universal interface.
This modality-adaptive module guarantees cooperation between the two modalities by projecting the verbal and visual modalities into a common semantic space while maintaining modality-specific characteristics. The team has presented a two-stage training paradigm for mPLUG-Owl2 that consists of joint vision-language instruction tuning and vision-language pre-training. With the help of this paradigm, the vision encoder has been made to collect both high-level and low-level semantic visual information more efficiently.
The team has conducted various evaluations and has demonstrated mPLUG-Owl2’s ability to generalize to text problems and multi-modal activities. The model demonstrates its versatility as a single generic model by achieving state-of-the-art performances in a variety of tasks. The studies have shown that mPLUG-Owl2 is unique as it is the first MLLM model to show modality collaboration in scenarios including both pure-text and multiple modalities.
In conclusion, mPLUG-Owl2 is definitely a major advancement and a big step forward in the area of Multi-modal Large Language Models. In contrast to earlier approaches that primarily concentrated on enhancing multi-modal skills, mPLUG-Owl2 emphasizes the synergy between modalities to improve performance across a wider range of tasks. The model makes use of a modularized network architecture, in which the language decoder acts as a general-purpose interface for controlling various modalities.
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The post Meet mPLUG-Owl2: A Multi-Modal Foundation Model that Transforms Multi-modal Large Language Models (MLLMs) with Modality Collaboration appeared first on MarkTechPost.
Large Language Models, with their human-imitating capabilities, have taken the Artificial Intelligence community by storm. With exceptional text understanding and generation skills, models like GPT-3, LLaMA, GPT-4, and PaLM have gained a lot of attention and popularity. GPT-4, the recently launched model by OpenAI due to its multi-modal capabilities, has gathered everyone’s interest in the
The post Meet mPLUG-Owl2: A Multi-Modal Foundation Model that Transforms Multi-modal Large Language Models (MLLMs) with Modality Collaboration appeared first on MarkTechPost. Read More AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Staff, Tech News, Technology, Uncategorized