Summary of Rulearena: a Benchmark For Rule-guided Reasoning with Llms in Real-world Scenarios, by Ruiwen Zhou et al.
RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios
by Ruiwen Zhou, Wenyue Hua, Liangming Pan, Sitao Cheng, Xiaobao Wu, En Yu, William Yang Wang
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce RuleArena, a novel benchmark designed to evaluate large language models’ (LLMs’) ability to follow complex rules in reasoning. The benchmark assesses LLMs’ proficiency in handling intricate natural language instructions that demand long-context understanding, logical reasoning, and accurate mathematical computation across three practical domains: airline baggage fees, NBA transactions, and tax regulations. RuleArena distinguishes itself from traditional rule-based reasoning benchmarks by extending beyond standard first-order logic representations and grounding insights into authentic, practical scenarios. The findings reveal several limitations in LLMs, including struggles to identify and apply the appropriate rules, difficulties with accurate mathematical computations, and poor performance overall. These results highlight significant challenges in advancing LLMs’ rule-guided reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces RuleArena, a new way to test how well big language models can follow complex rules. The model looks at three real-life situations: airline baggage fees, NBA transactions, and tax regulations. It wants to see if the big language models can understand these rules and use them correctly. The results show that the models have trouble doing this, especially with math problems. |
Keywords
» Artificial intelligence » Grounding
