RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios (2412.08972v2)

Abstract: This paper introduces RuleArena, a novel and challenging benchmark designed to evaluate the ability of 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. We also observe a significant performance boost when LLMs are provided with external tools for oracle math and logic operations. These results highlight significant challenges and promising research directions in advancing LLMs' rule-guided reasoning capabilities in real-life applications. Our codes and data are publicly available on https://github.com/skyriver-2000/RuleArena.

• The paper introduces RuleArena, a novel framework to evaluate LLMs' rule-guided reasoning in practical domains.
• It rigorously assesses LLM performance on 816 test problems across 95 diverse rules from tax, sports, and airline contexts.
• Insights reveal that even advanced LLMs struggle with rule recall and application, highlighting areas for future improvement.

Evaluation of Rule-Guided Reasoning in LLMs: A Critical Assessment of RuleArena

The paper introduces RuleArena, a benchmarking framework specifically designed to scrutinize LLMs' (LLMs) proficiency in rule-guided reasoning. The domains targeted by this benchmark include pressing practical areas such as airline baggage fees, NBA player transactions, and tax regulations, embodying real-world complexity that extends beyond first-order logic. This paper highlights the inherent challenges that LLMs face when attempting to leverage intricate rule-based systems, offering a thorough evaluation of the models' competencies and limitations in these settings.

Problem Formulation and Dataset Description

RuleArena evaluates the capacity of LLMs to navigate through complex rule sets, requiring not only a deep understanding of domain-specific knowledge but also the ability to accurately apply logical reasoning and perform mathematical computations. RuleArena's dataset consists of 95 rules from the three chosen domains, forming a basis for 816 test problems. The benchmark evaluates models on various difficulty levels, structured to challenge their understanding and application of rules within rich contextual frameworks.

Evaluation Metrics and Results

The evaluation is conducted using a suite of metrics to separately assess rule selection and rule application. The results indicate that state-of-the-art LLMs, like GPT-4o and Claude-3.5 Sonnet, struggle significantly with complex rule-guided reasoning. Despite high precision in some areas, such as reasoning accuracy in airline and tax domains, recall and overall problem-solving accuracy remain low. This discrepancy points to the models' difficulties in recalling and applying non-essential yet contextually critical rules.

Discussion and Analysis

The paper’s analysis exposes systematic deficiencies in existing LLMs, revealing primary challenges in rule integration and susceptibility to distractions. LLMs often fail not because of outright rule identification errors but due to inadequate recall of non-essential rules, improper computation, or confusion with similar rules under different conditions. The combination of these issues poses significant hurdles to achieving accurate and reliable results in real-world applications.

Implications and Future Research Directions

The practical implications of these findings are profound, especially in high-stakes domains where rule compliance is non-negotiable. The authors argue that improvement in LLM performance necessitates advancements in both rule recall mechanisms and the consistent application of intricate rule sets. Future work could benefit from exploring automated evaluation systems utilizing LLMs, designing new training paradigms with rule-guided data, and constructing hybrid frameworks that blend symbolic reasoning with statistical methods to elevate the robustness of rule-based reasoning.

Strategically, RuleArena serves as a crucial framework for evaluating progress in LLM research. It offers a clear lens through which researchers can assess the precision and limitations of models as they engage with real-world complexities. Addressing the challenges identified in the paper could significantly boost the reliability of LLMs in user-facing applications, reducing the risks associated with erroneous outputs.