InterGPS
Interpretable Geometry Problem Solver (Inter-GPS) with Geometry3K dataset of 3,002 geometry problems with dense annotation in formal language using theorem knowledge and symbolic reasoning
Phi-4-multimodal-instruct from Microsoft currently leads the InterGPS leaderboard with a score of 0.486 across 2 evaluated AI models.
What InterGPS measures
InterGPS is a text benchmark that evaluates large language models on math and spatial reasoning tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.4, with the leader reaching 0.5.
Compare leaders on the best AI for math and best AI for spatial reasoning leaderboards.
Publication
- Paper
- Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
- Authors
- Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, and 3 others
- Published
- arXiv
- 2105.04165
Abstract
Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods. The project with code and data is available at https://lupantech.github.io/inter-gps.
Phi-4-multimodal-instruct leads with 48.6%, followed by Phi-3.5-vision-instruct at 36.3%.
Progress Over Time
Interactive timeline showing model performance evolution on InterGPS
InterGPS Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Microsoft | 6B | — | — | ||
| 2 | Microsoft | 4B | — | — |
FAQ
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