Scheherazade: Evaluating Chain-of-Thought Math Reasoning in LLMs with Chain-of-Problems
<|reference_start|>Scheherazade: Evaluating Chain-of-Thought Math Reasoning in LLMs with Chain-of-Problems: Benchmarks are critical for measuring progress of math reasoning abilities of Large Language Models (LLMs). However, existing widely-used benchmarks such as GSM8K have been rendered less useful as multiple cutting-edge LLMs achieve over 94% accuracy. While harder benchmarks have been proposed, their creation is often manual and expensive. We present Scheherazade, an automated approach for producing challenging mathematical reasoning benchmarks by logically chaining mathematical reasoning problems. We propose two different chaining methods, forward chaining and backward chaining, which require reasoning forward and backward through the chain respectively. We apply Scheherazade on GSM8K to create GSM8K-Scheherazade and evaluate 3 frontier LLMs and OpenAI's o1-preview on it. We show that while frontier models' performance declines precipitously at only a few questions chained, a preliminary evaluation suggests o1-preview performance persists up to 5 questions chained backwards. In addition, while all other models perform worse when problems are chained backwards, o1-preview performs better on backward-chained benchmarks. We will release the dataset and code publicly.<|reference_end|>
@article{miner2024scheherazade:,
    title={Scheherazade: Evaluating Chain-of-Thought Math Reasoning in LLMs with
  Chain-of-Problems},
    author={Stephen Miner, Yoshiki Takashima, Simeng Han, Ferhat Erata, Timos
  Antonopoulos, Ruzica Piskac, Scott J Shapiro},
    journal={arXiv preprint arXiv:2410.00151},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00151},
    primaryClass={cs.CL}
}