This AI Paper introduces wings: Dual-Learn construction protects text-only to forget in multimorder models

Multimodal llMs: Expanding power throughout the text and view

Expanding large numbers of languages ​​(LLMS) to manage many modelities, especially photos and text, enabled the development of AI cooperation programs and accurate. Multimodal LLMS (MLLMs) can interpret material, answer questions about pictures, and share talks that include both text and pictures. Their visual and language fields make it more important for applications such as education, the appearance of content, and active assistants.

The only time to forget the text only in mlms

However, combining the idea in the llms creates a problem. When training in datasets mix pictures with the text, MLMMSs often lose their ability to manage text activities. This item, known as the Delight of the Scripture, is occurring because the visible tokens are included in the tracking of the model to divert the model away from the text. As a result, the MLLM begins to prioritize the content related to the picture and function properly in the activities that require only a language, such as the basic consideration, understanding or answering questions

The limitations of existing degrees

Several methods try to deal with this decreases. Others reorganize the largest data data at the time of training, while others take turns between the text-only and multimodal offer. These strategies aim to remind the model of its original language. Some projects include adapter layers or tuning faster. However, these processes often increase the cost of training, they need a complex idea of ​​flexibility, or fail to restore the understanding of the text completely. The problem is very evident how the model's attention changes when the pictures tokens are presented in order.

Introduced by the wings: Alaba and Nanjing University

Stainless investigators from the Alia Group Ai and Nanjing University have brought a new approach called the wings. The project added two viewers to visual and visual education – each part of the MLLM. These students work closely with the model's attention process. The structure is like “wings” attached to both sides of paychecks. The route section is controlled by each learner to find the taste from the current Token MIX, which allows the model to measure its focus between visual information and in power in power.

Lorra's low-quality attention (Lorra): Accumulation and awareness in the same way

The Wings Architecture uses a method called Low-Rank-Relidance Attention (Lorra), which keeps integration can be convinced while empowering students to obtain important information. In the first stage of training, only visible students are activated to synchronize the features of the image. In the second phase, visual and educational students are trained by a router module using metals to share responsibility. Each learner uses the appropriate attention blocks to contact this picture or the surrounding text, and its results are integrated with the main model. This ensures that visual attention does not report the understanding of the text.

Wings Perform Bancmarkr in the scripture in the text and multimodal activities

According to work, the wings showed powerful results. In MMLA Dataset, it only scored 60 .53 points, represent the development of 9.70 points compared to the same basic model. On the CMMLU, he found 69.82 points, 9.36 highest points than found. In factual tasks such as racial benchmarks the multimodal benchmarks like MMMMU-Val, the wings earned 4,78 points. It also shows strong results in the IIt Benchmark, to manage many mixed conversations and more variables effectively than some MLMS open at the same level.

Conclusion: With more MLLMs and usual

In short, researchers face the issue of catastrophe-only books that forget MLMMS with informing the wings, the construction of two dedicated students and dedication text alongside. By means of analyzing and designing targets, they maintain service performance while improving visual comprehension, providing a balanced and efficient multimodal function.

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