Large language models, with their human imitation capabilities, have taken the artificial intelligence community by storm. With exceptional text generation and understanding capabilities, models such as GPT-3, LLaMA, GPT-4, and PaLM have gained much attention and popularity. GPT-4, the model recently released by OpenAI due to its multimodal capabilities, has sparked everyone’s interest in the convergence of vision and language applications, as a result of which MLLM (Multi-modal Large Language Models) have been developed. MLLMs were introduced with the intention of improving them by adding visual problem-solving capabilities.
Researchers have focused on multimodal learning, and previous studies have found that multiple modalities can work well together to improve performance on textual and multimodal tasks at the same time. Currently existing solutions, such as intermodal alignment modules, limit the potential for cross-modal collaboration. Large language models are adjusted during multimodal instruction, compromising performance on text tasks, which is very challenging.
To address all these challenges, a team of researchers from Alibaba Group has proposed a new multimodal basic model called mPLUG-Owl2. mPLUG-Owl2’s modularized network architecture takes into account cross-modal interference and cooperation. This model combines common functional modules to foster cross-modal cooperation and a modality-adaptive module to seamlessly transition between multiple modalities. When doing this, it uses a language decoder as a universal interface.
This modality-adaptive module ensures 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 consisting of co-tuning of vision and language instruction and pre-training of vision and language. With the help of this paradigm, the vision encoder has been created to collect high-level and low-level semantic visual information more efficiently.
The team has conducted several evaluations and demonstrated the ability of mPLUG-Owl2 to generalize to text problems and multimodal activities. The model demonstrates its versatility as a single generic model by achieving state-of-the-art performances in a variety of tasks. Studies have shown that mPLUG-Owl2 is unique in that it is the first MLLM model to show modality collaboration in scenarios that include both pure text and multiple modalities.
In conclusion, mPLUG-Owl2 is definitely an important development and a big step forward in the area of large multimodal language models. Unlike previous approaches that primarily focused on improving multimodal skills, mPLUG-Owl2 emphasizes synergy between modalities to improve performance on a broader range of tasks. The model uses a modularized network architecture, in which the language decoder acts as a general-purpose interface to control various modalities.
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Tanya Malhotra is a final year student of University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with a burning interest in acquiring new skills, leading groups and managing work in an organized manner.
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