# A Graph-based Approach for Multi-Modal Question Answering from Flowcharts in Telecom Documents Source: [A Graph-based Approach for Multi-Modal Question Answering from Flowcharts in Telecom Documents]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:18:10.000Z Authors: Sumit Soman, H. G. Ranjani, Sujoy Roychowdhury, Venkata Dharma Surya Narayana Sastry, Akshat Jain, Pranav Gangrade, Ayaaz Khan Published: 2025-07-25T07:36:13.000Z ## Abstract Question-Answering (QA) from technical documents often involves questions whose answers are present in figures, such as flowcharts or flow diagrams. Text-based Retrieval Augmented Generation (RAG) systems may fail to answer such questions. We leverage graph representations of flowcharts obtained from Visual large Language Models (VLMs) and incorporate them in a text-based RAG system to show that this approach can enable image retrieval for QA in the telecom domain. We present the end-to-end approach from processing technical documents, classifying image types, building graph representations, and incorporating them with the text embedding pipeline for efficient retrieval. We benchmark the same on a QA dataset created based on proprietary telecom product information documents. Results show that the graph representations obtained using a fine-tuned VLM model have lower edit distance with respect to the ground truth, which illustrate the robustness of these representations for flowchart images. Further, the approach for QA using these representations gives good retrieval performance using text-based embedding models, including a telecom-domain adapted one. Our approach also alleviates the need for a VLM in inference, which is an important cost benefit for deployed QA systems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()