A New Approach to Training: Asking LMMs Questions Yields Better Results Than Transcription

['In a groundbreaking study, researchers at ByteDance Seed have discovered that training large multimodal models (LMMs) by asking them questions leads to more reliable results than traditional transcription methods, particularly when dealing with long, image-heavy documents. This approach enables a 7B model to outperform much larger models, even when faced with documents that are four times longer than any it encountered during training.', 'The conventional method of training LMMs involves having them transcribe text from lengthy documents, a process that can be both time-consuming and prone to errors. However, the ByteDance Seed team took a different approach, focusing on question-answering as a means of training.

By doing so, the model learns to identify relevant passages and provide accurate answers on its own, rather than simply transcribing pages of text.', 'The results of this study are noteworthy, as they demonstrate the potential for smaller, more efficient models to excel in tasks involving long-form content. According to the researchers, the 7B model was able to answer questions on long, image-heavy documents more reliably than much larger models. This has significant implications for the development of LMMs, suggesting that there may be a more effective way to train these models than the traditional transcription-based approach.', "The study's findings have the potential to revolutionize the way LMMs are trained, making them more efficient and effective in handling complex, long-form content.

As the researchers noted, their approach enables models to learn in a more autonomous and flexible way, which could lead to significant breakthroughs in areas such as document understanding and question-answering.", 'Further research is needed to fully explore the potential of this approach, but the initial results are certainly promising. As the field of natural language processing continues to evolve, it will be interesting to see how this new approach to training LMMs develops and whether it will become a standard technique in the industry.']