Text2Arch: A Dataset for Generating Scientific Architecture Diagrams from Natural Language Descriptions

• URL: http://arxiv.org/abs/2604.14941v1
• Date: Thu, 16 Apr 2026 12:36:22 GMT
• Title: Text2Arch: A Dataset for Generating Scientific Architecture Diagrams from Natural Language Descriptions
• Abstract summary: A system that automatically generates scientific architecture diagrams from text with high semantic fidelity can be useful in multiple applications.<n>No clean large-scale open-access dataset exists, implying lack of any effective open models for this task.<n>We make the code, data and models publicly available.
• Score: 8.259454114005989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Communicating complex system designs or scientific processes through text alone is inefficient and prone to ambiguity. A system that automatically generates scientific architecture diagrams from text with high semantic fidelity can be useful in multiple applications like enterprise architecture visualization, AI-driven software design, and educational content creation. Hence, in this paper, we focus on leveraging language models to perform semantic understanding of the input text description to generate intermediate code that can be processed to generate high-fidelity architecture diagrams. Unfortunately, no clean large-scale open-access dataset exists, implying lack of any effective open models for this task. Hence, we contribute a comprehensive dataset, \system, comprising scientific architecture images, their corresponding textual descriptions, and associated DOT code representations. Leveraging this resource, we fine-tune a suite of small language models, and also perform in-context learning using GPT-4o. Through extensive experimentation, we show that \system{} models significantly outperform existing baseline models like DiagramAgent and perform at par with in-context learning-based generations from GPT-4o. We make the code, data and models publicly available.

Related papers

• Deep Graph-Language Fusion for Structure-Aware Code Generation [3.441352304853801]
  CGFuse is a novel framework that enables token-level integration of graph-derived representations by infusing learned graph features directly into pre-trained language models.<n>We demonstrate up to 10-16% BLEU and 6-11% CodeBLEU improvements in code generation performance.
  arXiv  Detail & Related papers  (2026-05-05T12:33:20Z)
• NAG: A Unified Native Architecture for Encoder-free Text-Graph Modeling in Language Models [33.49410203951687]
  We argue this approach is suboptimal for text-graphs.<n>NAG (Native Architecture for Graphs) is a unified framework that internalizes graph processing within the Language Models.<n>NAG achieves robust graph comprehension without the overhead of external encoders.
  arXiv  Detail & Related papers  (2026-01-30T07:22:11Z)
• UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters [55.34921520578968]
  Vision-language models (VLMs) have achieved impressive unified recognition of text and formulas.<n>We propose UniRec-0.1B, a unified recognition model with only 0.1B parameters.<n>It is capable of performing text and formula recognition at multiple levels, including characters, words, lines, paragraphs, and documents.
  arXiv  Detail & Related papers  (2025-12-24T10:35:21Z)
• Improving Large Vision-Language Models' Understanding for Field Data [62.917026891829025]
  We introduce FieldLVLM, a framework designed to improve large vision-language models' understanding of field data.<n>FieldLVLM consists of two main components: a field-aware language generation strategy and a data-compressed multimodal model tuning.<n> Experimental results on newly proposed benchmark datasets demonstrate that FieldLVLM significantly outperforms existing methods in tasks involving scientific field data.
  arXiv  Detail & Related papers  (2025-07-24T11:28:53Z)
• Emergent Visual-Semantic Hierarchies in Image-Text Representations [13.300199242824934]
  We study the knowledge of existing foundation models, finding that they exhibit emergent understanding of visual-semantic hierarchies. We propose the Radial Embedding (RE) framework for probing and optimizing hierarchical understanding.
  arXiv  Detail & Related papers  (2024-07-11T14:09:42Z)
• mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding [100.17063271791528]
  We propose the Unified Structure Learning to boost the performance of MLLMs. Our model DocOwl 1.5 achieves state-of-the-art performance on 10 visual document understanding benchmarks.
  arXiv  Detail & Related papers  (2024-03-19T16:48:40Z)
• Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text [3.114960935006655]
  This paper aims to improve the state of the art of automatic construction and completion of knowledge graphs from text. In this context, one emerging paradigm is in-context learning where a language model is used as it is with a prompt.
  arXiv  Detail & Related papers  (2023-05-15T17:10:19Z)
• Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [57.854498238624366]
  We propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP) for data-efficient knowledge graph construction. RAP can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample.
  arXiv  Detail & Related papers  (2022-10-19T16:40:28Z)
• KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs [26.557447199727758]
  We propose a novel knowledge-aware language model framework based on fine-tuning process. Our model can efficiently incorporate world knowledge from KGs into existing language models such as BERT.
  arXiv  Detail & Related papers  (2021-09-09T12:39:17Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.