This episode introduces LOGICGAME, a benchmark designed to assess the rule-based reasoning abilities of Large Language Models (LLMs). LOGICGAME tests models in two key areas:
1. Execution: Single-step tasks where models apply rules to manipulate strings or states.
2. Planning: Multi-step tasks requiring strategic thinking and decision-making.The benchmark includes tasks of increasing difficulty (Levels 0-3) and evaluates models based on both their final answers and reasoning processes.
Key Findings:
- Even top LLMs struggle with complex tasks, achieving only around 20% accuracy overall and less than 10% on the most difficult tasks.
- Few-shot learning improves performance in execution tasks but has mixed results in planning tasks.
- A case study on the Reversi game reveals that LLMs often fail to grasp core mechanics.Conclusion: While LLMs show promise, their ability to handle complex, multi-step rule-based reasoning needs significant improvement.
https://arxiv.org/pdf/2408.15778