NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry
Recent advances in large language models (LLMs) have demonstrated strong capabilities in natural language understanding and mathematical reasoning. However, their ability to translate informal mathematical problems into formal representations remains underexplored. This limitation is particularly important for neuro-symbolic geometry systems such as AlphaGeometry, whose theorem-proving engine requires inputs in a specialized domain-specific language (DSL). Although AlphaGeometry achieves near-IMO gold-medalist performance, manually converting natural-language problems into its formal syntax re
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- FuzzyOverlapping authors or contributors · 62%TauricResearch/TradingAgents →
“Shared author/contributor keys: xiao”
- LinkedLinked via arxiv author · 85%Samuel Xiao →
“NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry”
- LinkedLinked via arxiv author · 85%Judy Song →
“NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry”
- LinkedLinked via arxiv author · 85%Rory Hu →
“NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry”
- LinkedLinked via arxiv author · 85%Ziliang Zong →
“NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry”