Harnessing Webpage UIs for Text-Rich Visual Understanding
This work addresses the challenge of processing dense textual content integrated with visuals for multimodal models, with incremental improvements in web UI tasks and generalization to non-UI domains.
The authors tackled the problem of text-rich visual understanding in multimodal large language models by synthesizing multimodal instructions from webpage UIs using text-based LLMs, resulting in up to a 48% improvement on VisualWebBench and a 19.1% boost in element accuracy on Mind2Web.
Text-rich visual understanding-the ability to process environments where dense textual content is integrated with visuals-is crucial for multimodal large language models (MLLMs) to interact effectively with structured environments. To enhance this capability, we propose synthesizing general multimodal instructions from webpage UIs using text-based large language models (LLMs). Despite lacking direct visual input, text-based LLMs are able to process structured text representations from webpage accessibility trees. These instructions are then paired with UI screenshots to train multimodal models. We introduce MultiUI, a dataset containing 7.3 million samples from 1 million websites, covering diverse multimodal tasks and UI layouts. Models trained on MultiUI not only excel in web UI tasks-achieving up to a 48% improvement on VisualWebBench and a 19.1% boost in element accuracy on a web agent dataset Mind2Web-but also generalize surprisingly well to non-web UI tasks and even to non-UI domains, such as document understanding, OCR, and chart interpretation. These results highlight the broad applicability of web UI data for advancing text-rich visual understanding across various scenarios.