GRAM: Global Reasoning For Multi-page VQA

Blau Tsachi, Fogel Sharon, Ronen Roi, Golts Alona, Ganz Roy, Avraham Elad Ben, Aberdam Aviad, Tsiper Shahar, Litman Ron. Arxiv 2024

[Paper]    
Ethics And Bias Model Architecture Pretraining Methods RAG Training Techniques Transformer

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our compression-transformer (C-Former),reducing the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM’s state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

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