RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios

2024-12-12

arXiv.org

AI Summary

Large language models (LLMs) exhibit limited capability in comprehending long contexts, performing logical reasoning, and executing mathematical computations under complex, real-world natural language rules. Method: We introduce RuleArena—the first benchmark grounded in authentic business rules (airline baggage fees, NBA trades, tax regulations)—designed to evaluate rule-following proficiency beyond classical first-order logic benchmarks. RuleArena emphasizes ambiguity identification, cross-rule discrimination, and practical reliability, incorporating multi-domain rule formalization, human-annotated test sets, and a fine-grained error attribution framework. Contribution/Results: Extensive evaluation reveals that state-of-the-art LLMs consistently suffer from rule misselection, computational inaccuracy, and poor generalization across semantically similar rules, significantly underperforming human experts across all tasks. RuleArena establishes a novel, reproducible paradigm for assessing and advancing rule-following competence in LLMs.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper introduces RuleArena, a novel and challenging benchmark designed to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. Covering three practical domains -- airline baggage fees, NBA transactions, and tax regulations -- RuleArena assesses LLMs' proficiency in handling intricate natural language instructions that demand long-context understanding, logical reasoning, and accurate mathematical computation. Two key attributes distinguish RuleArena from traditional rule-based reasoning benchmarks: (1) it extends beyond standard first-order logic representations, and (2) it is grounded in authentic, practical scenarios, providing insights into the suitability and reliability of LLMs for real-world applications. Our findings reveal several notable limitations in LLMs: (1) they struggle to identify and apply the appropriate rules, frequently becoming confused by similar but distinct regulations, (2) they cannot consistently perform accurate mathematical computations, even when they correctly identify the relevant rules, and (3) in general, they perform poorly in the benchmark. These results highlight significant challenges in advancing LLMs' rule-guided reasoning capabilities in real-life applications.

Problem

• Evaluates LLMs' ability to follow complex real-world rules
• Assesses LLMs' proficiency in long-context understanding and logical reasoning
• Identifies limitations in LLMs' rule application and mathematical computation

Innovation

• RuleArena benchmark for real-world rule-guided reasoning
• Assesses LLMs in long-context understanding and logic
• External tools boost math and logic performance