One big step forward in creating generalist models is the looks of Large Language Models (LLMs). Their astounding text understanding and generation performances are sometimes based on the Transformer architecture and a single next-token prediction aim. Nevertheless, they’re currently hampered by their inability to access information outside the text. This emphasizes the requirement for reliable multimodal models able to performing various tasks using various modalities. 

Recent efforts have sought to enhance task/modality-specific techniques by constructing multimodal models with more power. A couple of of those methods seek to incorporate greater than two modalities, reminiscent of image/video-text, although most of those efforts are dedicated to image-text jobs. 

To handle this problem, the researchers at Sorbonne University began by developing general-purpose models that may address any problem. They introduce UnIVAL, a way that avoids counting on any single modality. UnIVAL integrates two modalities and all 4 (text, pictures, video, and audio).

UnIVAL is the primary model to resolve picture, video, and audio language challenges with a unified architecture, vocabulary, input/output format, and training aim without requiring massive amounts of information for training or massive model size. The 0.25 billion parameter model delivers performance on par with prior art tailored to a certain modality. The researchers obtained latest SoTA on several jobs with similarly sized models. 

Their research into the interplay and transfer of information between pretrained tasks and modalities demonstrates the worth of multitask pretraining in comparison with traditional single-task pretraining. In addition they discover that pretraining the model on additional modalities improves its generalization to untrained modalities. Particularly, when fine-tuned on audio-text problems, UnIVAL can achieve competitive performance to SoTA without audio pretraining. 

Based on previous studies, the team also presents a brand new investigation into merging multimodal models by weight interpolation. They display that interpolation in the load space may successfully mix the talents of the multiple fine-tuned weights, creating more robust multitask models with none inference overhead when using the unified pretrained model for various multimodal tasks. The range of multimodal activities can thus be used and recycled by averaging various fine-tuned weights and multitasking pretraining. Weight interpolation has never been tested with multimodal baseline models before, but this research is the primary to successfully achieve this.

The researchers also mention two significant drawbacks of UnIVAL:

1. UnIVAL is at risk of hallucinations. Particularly, it might invent latest objects in visual descriptions (object bias), giving more weight to consistency than accuracy. 
2. It has trouble following elaborate directions. They found that the model underperformed when given complex instructions, reminiscent of picking out one object from a gaggle of comparable ones, finding things which are distant or extremely close, or recognizing numbers.

The researchers hope their findings will motivate other scientists and speed up the means of constructing latest modality-agnostic generalist assistant agents. 

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