A Graph-based Approach for Multi-Modal Question Answering from Flowcharts in Telecom Documents

TL;DR

This paper introduces a graph-based method leveraging visual language models to improve question-answering accuracy from flowcharts in telecom documents, integrating image and text data for better retrieval.

Contribution

It presents an end-to-end system that combines graph representations of flowcharts with text-based retrieval, reducing reliance on costly visual models during inference.

Findings

Graph representations have lower edit distance to ground truth.

The approach improves retrieval performance in telecom QA.

Cost-effective by reducing VLM inference dependency.

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.