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A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

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arxiv 2302.11097 v2 pith:HTMBI7QH submitted 2023-02-22 cs.AI cs.CV

Ming-Liang Zhang , Fei Yin , Cheng-Lin Liu This is my paper · ORCID
classification cs.AIcs.CV
keywords diagramgeometricneuraldatasetmulti-modalclausesfusiongeometry
verification ladder T0 review T1 audit T2 compute T3 formal
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Geometry problem solving (GPS) is a high-level mathematical reasoning requiring the capacities of multi-modal fusion and geometric knowledge application. Recently, neural solvers have shown great potential in GPS but still be short in diagram presentation and modal fusion. In this work, we convert diagrams into basic textual clauses to describe diagram features effectively, and propose a new neural solver called PGPSNet to fuse multi-modal information efficiently. Combining structural and semantic pre-training, data augmentation and self-limited decoding, PGPSNet is endowed with rich knowledge of geometry theorems and geometric representation, and therefore promotes geometric understanding and reasoning. In addition, to facilitate the research of GPS, we build a new large-scale and fine-annotated GPS dataset named PGPS9K, labeled with both fine-grained diagram annotation and interpretable solution program. Experiments on PGPS9K and an existing dataset Geometry3K validate the superiority of our method over the state-of-the-art neural solvers. Our code, dataset and appendix material are available at \url{https://github.com/mingliangzhang2018/PGPS}.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning

    cs.CV 2024-12 reject novelty 6.0 of 10

    Geo-LLaVA combines retrieval-augmented fine-tuning with in-context learning, reporting 65.25% and 42.36% on selected subsets of GeoQA and the new GeoMath dataset.

  2. RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?

    cs.LG 2025-01 reject novelty 5.0 of 10

    Long-CoT fine-tuning with QwQ-generated traces improves math and geometry benchmarks, and RedStar-DPO surpasses QwQ on AIME24, but several reported numbers conflict with the paper's own tables.

  3. Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring

    cs.AI 2024-12 conditional novelty 5.0 of 10

    GPSM4K is a new geometry QA dataset with step-by-step solutions; the paper reports that fine-tuning on it improves LVLM scores, with captioning and RAG giving smaller gains.

  4. Towards Geometry Problem Solving in the Large Model Era: A Survey

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A survey that organizes geometry problem-solving research into benchmark construction, parsing, and reasoning, and proposes a unified parse-then-reason paradigm for the large-model era.

  5. Visual question answering: from early developments to recent advances -- a survey

    cs.CV 2025-01 conditional novelty 2.0 of 10

    A survey that classifies VQA architectures by encoder, fusion, and decoder, reviews datasets and metrics, and discusses applications and future directions.

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