Title: Text-to-Code Generation for Modular Building Layouts in Building Information Modeling
Venue: NeurIPS 2025
Accepted: yes
Keywords: Building layout design; Modular construction; Building information modeling; Code generation; Large language models
Abstract: We present Text2MBL, a text-to-code generation framework that generates executable Building Information Modeling (BIM) code directly from textual descriptions of modular building layout (MBL) design. Unlike conventional layout generation approaches that operate in 2D space, Text2MBL produces fully parametric, semantically rich BIM layouts through on‑the‑fly code instantiation. To address MBLs' unique challenges due to their hierarchical three-tier structure: modules (physical building blocks), units (self-contained dwellings), and rooms (functional spaces), we developed an object-oriented code architecture and fine-tuned large language models to output structured action sequences in code format. To train and evaluate the framework, we curated a dataset of paired descriptions and ground truth layouts drawn from real‑world modular housing projects. Performance were assessed using metrics for executable validity, semantic fidelity, and geometric consistency. By tightly unifying natural language understanding with BIM code generation, Text2MBL establishes a scalable pipeline from high-level conceptual design to automation-ready modular construction workflows. Our implementation is available at https://github.com/CI3LAB/Text2MBL.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new method for generating BIM models in a text-guided manner by representing BIM as executable code and framing the generation task as a sequence-to-sequence problem. The main technical contributions include:\n-\tA code representation scheme specifically designed based on Modular Building Layout (MBL) principles.\n-\tA newly curated dataset containing paired textual descriptions and corresponding structured BIM code.\n\nThe authors fine-tuned a large language model using the proposed dataset to generate structured BIM code, and evaluated the performance using metrics related to code validity, semantic fidelity, and geometric consistency. The experimental results demonstrate the effectiveness of the proposed code representation in enabling autonomous BIM generation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper is well-written and clearly presented.\n- The technical contribution centers on the design of a code representation tailored for BIM generation, which aligns well with MBL modeling principles. In addition, the authors have curated a new dataset consisting of paired textual descriptions and corresponding executable BIM codes, which is an important resource that supports future research in this area.\n- The experimental evaluation is relatively comprehensive, covering multiple aspects such as code validity, semantic fidelity, and geometric consistency.\n\nWeakness:\n- The model architecture lacks significant innovation. The approach mainly relies on fine-tuning an existing large language model using a newly constructed dataset, without introducing substantial modifications to the underlying architecture or training methodology. As a result, the technical novelty of the work may be limited. Additionally, the reviewer would like to raise a few concerns about the performance of the large language model; see “questions” for more details.\n\nQUESTIONS:\n1. It would be helpful to understand how the large language model performs in a zero-shot setting. Specifically, could the authors report the performance of the pre-trained model (without fine-tuning) under both the proposed code representation and the coordinate-based representation? Such a comparison would not only help validate whether the proposed code representation is more suitable for the task but also provide insights into the effectiveness of the proposed dataset.\n\n2. What is the generalization capability of the fine-tuned model? Since the model is trained on a specific dataset, there is a risk that it may overfit to the style or phrasing patterns in the training examples. The reviewer would like to know how well the model performs when given free-form, less structured textual inputs — for instance, a broad requirement such as: \"I need a house with a living room, three bedrooms, a bathroom, a kitchen, and a balcony.\"\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nMotivated by applications in user-inclusive design in construction workflow, this paper presents Text2MBL, a text-to-code generation framework that takes in textual description and generates executable Building Information Modeling (BIM) code for modular building layout (MBL) design. \n\nTo account for MBLs’ hierarchical three-tier structure (module, unit, and room), the authors design a specific object-oriented code structure and fine-tune LLMs to generate code of this structure. Specifically, the output takes the form of action sequences that when execuetd in BIM environment produce MBLs.\n\nTo fine-tune the LLM, data is curated from real-world housing projects, and synthetic data is shown to enhance peformance significantly when used for augmentation.\n\nText2MBL is shown to outperform conventional coordinate-based approaches in geometric consistency, and perform strongly on executable validity, semantic fidelity.\n\nSTRENGTHS AND WEAKNESSES:\nQuality\n- [Strength] The method used (synthetic data augmentation, code format, LLM finetuning)  is not particularly complex but is well grounded in the application and well supported by experimental result.\n- [Strength] Result analysis is comprehensive.\n- [Strength] There are multiple discussions of limitations.\n\nClarity\n- [Strength] The paper is generally well written, and a comprehensive appendix is provided.\n\nSignificance\n- [Strength] This work is clearly well grounded in application, and practitioners can likely benefit from it.\n- [Strength] Code and data are both released, and a functional plug-in is deployed on a representative BIM platform. Deployed as detailed in Appendix H.\n- [Weakness] The current model is not particularly expressive, which can be a topic of future work.\n\nOriginality\n- [Strength] The specific task, generating BIM-based MBL from text is underexplored, and this work proposes a new method that outperforms existing method.\n- [Weakness] There are lines of works in the vision community that tackeles the general text-to-scene task that are not mentioned as related works. For example, “AnyHome” generates house floorplan from text and eventually a detailed mesh, and “LLplace” and “Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases” generate code or structured program for 1-room scene. Constraints for modular construction can likely be inserted by fine-tuning the LLM or modifying the prompt.\n- [Weakness] The high-level insight that code-driven approach is effective has been discussed in various works in the LLM community.\n\nQUESTIONS:\n- The only baseline is coordinate-based approach. Is this predominantly limited by the fact that few existing methods output BIM-based MBLs? Is it entirely unreasonable to compare with other text-to-floorplan methods (in aspects such as no missing entity, correct inter-entity relation, geometric consistency IoU, etc.)?\n- It is mentioned that critical factors such as element placement, clash avoidance, component order, and support for irregular geometries, are addressed by Text2MBL by “abstracting complex design operations into high-level interfaces” (line 223-224). What exactly does this mean?\n- The relationship between BIM and MBL is not explicitly explained. How does “BIM model” relate to “MBL” (line 102-103)? What is different about “BIM-based MBL” compared to standard MBL?  Also, for someone not familiar with the specific application domain, the use of “BIM” (without definition up front) in the introduction can be a source of confusion.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose Text2MBL, a text-to-code generation framework that translates natural language descriptions of modular building layouts (MBLs) into executable code for Building Information Modeling (BIM) systems. Addressing the unique challenges posed by MBLs (e.g., hierarchical structures, spatial relationships, and semantic complexity), the authors design a proof-of-concept implementation in Autodesk Revit. They curate a dataset of 198 MBL designs paired with multiple textual descriptions and code sequences, including both real and synthetic examples. Experiments with fine-tuned Qwen2.5 models show that the code-output approach outperforms coordinate-output baselines in terms of compile rate, semantic fidelity (F1 scores), and geometric consistency (IoU). Additional analyses demonstrate the benefit of synthetic data augmentation and the challenges associated with component complexity.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. **Novel integration of BIM, code generation, and spatial design**: The paper introduces a new task and methodology for text-to-BIM code generation, which bridges natural language understanding and structured building architectural modeling.\n2. **Comprehensive system implementation and dataset curation**: The authors provide a full implementation in Autodesk Revit with detailed class designs and a paired dataset of real and synthetic MBLs, facilitating both reproducibility and future research.\n3. **Strong empirical results and insightful error analysis**: Across multiple metrics and model scales, the proposed code-driven generation method consistently outperforms coordinate-based baselines. The authors provide fine-grained error analysis and show effective use of synthetic data in low-resource scenarios.\n\n**Weaknesses**\n\n1. **Lack of external validation and user-centric evaluation**: The work would benefit from qualitative assessment by domain experts (e.g., architects, engineers) or user studies to evaluate the practical value of the generated outputs. Table 2 reports very high scores for top models—does this indicate that the benchmark is too easy and already saturated, or that the model is close to being deployable in real-world settings?\n2. **Insufficient discussion of related LLM-based design tools**: The paper briefly mentions works like Text2BIM and Tell2Design, but does not provide detailed comparisons. Additionally, recent research on LLM-guided scientific graphics generation, such as *AutomaTikZ* [A] and *DiagrammerGPT* [B], is highly relevant and should be discussed to position the contribution more clearly within the broader landscape of text-to-structured-design systems\n\n[A] Belouadi et al., AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ, ICLR 2024\n\n[B] Zala et al., DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning, COLM 2024\n\nQUESTIONS:\n- As a non-expert in the architecture and construction domains, I found myself wondering how representative the use of Autodesk Revit is for real-world applications. Given that previous works like Text2BIM employed Vectorworks, could the authors elaborate on what BIM tools are most widely adopted in industry? Specifically, what domain-specific languages or software platforms are considered essential or standard in BIM-based workflows?\n\nLIMITATIONS:\nNo specific concern", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a framework called Text2MBL, which aims to directly generate executable Building Information Modeling (BIM) code from textual descriptions of modular building layout (MBL) design. The framework leverages an object-oriented code architecture to address the unique challenges posed by the three-tiered hierarchical structure of MBLs (modules, units, and rooms), which traditional 2D layout generation methods struggle to handle. To train and evaluate Text2MBL, the authors created a dataset consisting of paired real-world modular housing project descriptions and BIM layouts, and fine-tuned large language models (from the Qwen2.5 family) to output structured action sequences in code.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The unique “module-unit-room” hierarchy of MBLs introduces structural constraints and complexity. Text2MBL explicitly targets these issues through its tailored design.\n2.  The paper builds a dataset based on real-world modular housing projects, pairing textual descriptions with BIM code. To mitigate data scarcity, both partially and fully synthetic data generation strategies are adopted.\n\nWeaknesses: \n\n1. The paper fails to compare with key related methods, especially **Text2BIM**. It is unclear how Text2MBL extends, improves upon, or addresses challenges not solved by Text2BIM (such as MBL’s inherent hierarchy and constraints).\n2. As shown in Figure 1, the current framework requires users to specify highly detailed spatial parameters—such as precise module dimensions (length, width), door positions (e.g., on the south wall, 2950mm from the west side), and alignment configurations (e.g., south wall, 2950mm from the west side). Requires very specific and detailed input, which will not be the preferred form of input for actual users.\n3. Although the authors fine-tune multiple variants from the Qwen2.5 family and briefly mention GPT-4.1-mini in the appendix, the paper lacks a more systematic comparison of different large language models on the proposed task.\n4. While the introduction explicitly states that “design decision-making is no longer designer-exclusive but user-inclusive” (line 27) and that textual descriptions are intended to “facilitate more intuitive and accessible user interaction” (line 28), the paper includes no user study or usability evaluation (e.g., user satisfaction, interaction efficiency). The Discussion section (Section 5) relegates user-centered evaluation to future work, creating a disconnect between the paper’s stated motivation and its actual methodology.\n\nQUESTIONS:\n1. Based on the examples provided (e.g., the rectangular modules in Figure 1 and Table 1) and the descriptions of the Module class (e.g., `rectangular geometry`)  and Utils class (e.g., `MidPointForRectangle`, `PointInRectangle`) in Appendix B, the system appears primarily designed for regular geometric shapes such as rectangles. However, real-world architectural designs often include irregular geometries (e.g., curved walls or polygonal modules). Can the current system handle such non-rectilinear geometries? If not, how extensible is the current code architecture to support more complex geometric forms?\n2. How can the input burden be lowered to support more natural and conceptual language? The introduction states that the use of text is intended to “facilitate more intuitive and accessible user interaction,” but the current framework (as shown in Figure 1) requires very specific geometric and spatial inputs. Can the model understand more abstract or high-level descriptions, such as “create a spacious living room connected to a kitchen”? If not, how might the system be extended in this direction?\n3. The \"Text2BMI\" in L49 is confusing.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}