HOLMES: Evaluating Higher-Order Logical Reasoning in LLMs
📰 ArXiv cs.AI
Learn to evaluate higher-order logical reasoning in LLMs using HOLMES, a new benchmark for real-world explainable symbolic reasoning
Action Steps
- Apply HOLMES to evaluate the higher-order logical reasoning of an LLM
- Run experiments to compare the performance of different LLMs on HOLMES
- Configure the HOLMES benchmark to test specific aspects of logical reasoning
- Test the ability of an LLM to reason over rules, predicates, and functions using HOLMES
- Analyze the results of HOLMES to identify areas for improvement in LLMs
Who Needs to Know This
AI researchers and developers can use HOLMES to assess and improve the logical reasoning capabilities of their LLMs, while data scientists and ML engineers can apply HOLMES to evaluate the performance of their models
Key Insight
💡 HOLMES is the first real-world benchmark for evaluating higher-order logical reasoning in LLMs, enabling more accurate assessment of AI reliability
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🤖 Evaluate higher-order logical reasoning in LLMs with HOLMES, a new benchmark for real-world explainable symbolic reasoning 💡
Key Takeaways
Learn to evaluate higher-order logical reasoning in LLMs using HOLMES, a new benchmark for real-world explainable symbolic reasoning
Full Article
Title: HOLMES: Evaluating Higher-Order Logical Reasoning in LLMs
Abstract:
arXiv:2606.23238v1 Announce Type: new Abstract: Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first-order-logic-centric, focusing on object-level deduction over fixed predicates. This misses many realistic scenarios where models must reason over rules, predicates, functions, constraints, and decision procedures themselves. We introduce HOLMES (Higher-Order Logic Meets real-world Explainable Symbolic reasoning), the first real-world benchmark for higher-order
Abstract:
arXiv:2606.23238v1 Announce Type: new Abstract: Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first-order-logic-centric, focusing on object-level deduction over fixed predicates. This misses many realistic scenarios where models must reason over rules, predicates, functions, constraints, and decision procedures themselves. We introduce HOLMES (Higher-Order Logic Meets real-world Explainable Symbolic reasoning), the first real-world benchmark for higher-order
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