Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for solving geometric problems are either small in scale or not publicly available. |
| Approach: | They propose a large-scale benchmark for geometric problem solving using formal language and symbolic reasoning. |
| Outcome: | The proposed approach parses geometry problems into formal language and performs symbolic reasoning step by step. |
Similar Papers
Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language (2026.findings-acl)
Copied to clipboard
Peijie Wang, Ming-Liang Zhang, Jun Cao, Chao Deng, Dekang Ran, Pi Bu, Hongda Sun, Xuan Zhang, Yingyao Wang, Jun Song, Bo Zheng, Fei Yin, Cheng-Lin Liu
| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various vision reasoning tasks. |
| Approach: | They propose a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations. |
| Outcome: | The proposed language achieves state-of-the-art parsing performance and significantly boosts MLLMs’ capabilities for downstream geometry reasoning tasks. |
GOLD: Geometry Problem Solver with Natural Language Description (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for solving geometry math problems struggle with accurately interpreting geometry diagrams, posing a challenge for problem-solving. |
| Approach: | They propose a model that extracts geometric relations from diagrams and converts them into natural language descriptions. |
| Outcome: | The proposed model outperforms the previous best method on the UniGeo dataset by 12.7% and 42.1% in calculation and proving subsets. |
GeoDRL: A Self-Learning Framework for Geometry Problem Solving using Reinforcement Learning in Deductive Reasoning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for automated geometry problem solving lack labeled data. |
| Approach: | They propose a framework that integrates logic graph deduction and deep reinforcement learning to optimize geometry reasoning as a Markov Decision Process. |
| Outcome: | The proposed framework improves accuracy and interpretability in the Geometry3K dataset while maintaining correctness. |
LANS: A Layout-Aware Neural Solver for Plane Geometry Problem (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing neural solvers take GPS as vision-language task but lack layout awareness . Existing models are criticized for complex rules and poor adaptability . |
| Approach: | They propose a layout-aware neural solver called LANS that integrates two modules to solve GPS. |
| Outcome: | The proposed solver outperforms existing neural and symbolic solvers on two datasets. |
Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to NLP are static and require manual formalization. |
| Approach: | They propose an adaptive, multi-paradigm, neuro-symbolic inference framework that automatically identifies formal reasoning strategies from problems expressed in natural language and dynamically selects and applies specialized formal logical solvers. |
| Outcome: | The proposed framework outperforms baselines on individual and multi-paradigm reasoning tasks by 17% and 6%. |
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021.acl-long)
Copied to clipboard
| Challenge: | Existing solutions for math word problems lack explicit integration of math symbolic constraints, leading to unexplainable and unreasonable predictions. |
| Approach: | They propose a novel mathematical model that explicitly incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
| Outcome: | The proposed solver incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on geometry problem solving treats calculation and proving as two specific tasks hindering a deep model to unify reasoning ability on multiple math tasks. |
| Approach: | They propose a large-scale Unified Geometry problem benchmark to unify geometry on multiple math tasks. |
| Outcome: | The proposed framework outperforms the existing model with 5.6% and 3.2% accuracies on calculation and proving problems. |
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to solve geometric problems are dependent on handcraft rules and limited on small-scale datasets. |
| Approach: | They propose a Geometric Question Answering dataset with 5,010 geometric problems with corresponding annotated programs to illustrate the solving process. |
| Outcome: | The proposed method is significantly lower than human performance on the proposed dataset than on a publicly available dataset. |
An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for solving geometric problems are limited due to lack of high-quality datasets and efficient neural solvers. |
| Approach: | They propose to annotate 2,518 geometric problems with richer types and greater difficulty using a benchmark dataset. |
| Outcome: | The proposed method improves the accuracy of automatic geometric problem solving to 66.09%. |
GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Various vision-language models (VLMs) have made significant progress in multimodal tasks, but they still struggle with geometry problems. |
| Approach: | They propose a vision-language model that leverages modular code-finetuning to generate and execute code using a predefined geometry function library. |
| Outcome: | The proposed model improves geometric reasoning abilities by 16% on a GeomVerse dataset compared to other methods. |