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

RuleArena exposes how LLMs fumble with basic rule-following tasks we humans take for granted.

RuleArena tests LLMs' ability to follow complex real-world rules across airline baggage fees, NBA transactions, and tax regulations, revealing significant gaps in rule-guided reasoning capabilities.

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https://arxiv.org/abs/2412.08972

🤔 Original Problem:

LLMs often generate unfaithful or misleading information when handling domain-specific tasks, leading to significant risks in real-world applications. Current benchmarks focus mainly on stylistic constraints rather than complex rule-following abilities.

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🔍 Solution in this Paper:

→ RuleArena introduces 95 authentic rules from three real-world domains to test LLMs' rule-following capabilities

→ The benchmark contains 816 test problems with varying difficulty levels

→ Each problem requires understanding multiple rules, performing mathematical computations, and applying logical reasoning

→ Novel evaluation metrics measure both rule selection accuracy and application correctness

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💡 Key Insights:

→ LLMs struggle to identify appropriate rules from large rule sets

→ Models get confused by similar but distinct rules

→ Computational errors occur even when rules are correctly identified

→ Performance degrades significantly with complex problems

→ Distracting rules significantly impact model performance

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📊 Results:

→ Even advanced models like GPT-4 and Claude-3.5 fail on complex tasks

→ Problem-wise recall strongly correlates with overall accuracy

→ Rule application correctness never reaches 100%

→ Performance drops sharply as problem complexity increases