Tree of Problems: Improving structured problem solving with compositionality
<|reference_start|>Tree of Problems: Improving structured problem solving with compositionality: Large Language Models (LLMs) have demonstrated remarkable performance across multiple tasks through in-context learning. For complex reasoning tasks that require step-by-step thinking, Chain-of-Thought (CoT) prompting has given impressive results, especially when combined with self-consistency. Nonetheless, some tasks remain particularly difficult for LLMs to solve. Tree of Thoughts (ToT) and Graph of Thoughts (GoT) emerged as alternatives, dividing the complex problem into paths of subproblems. In this paper, we propose Tree of Problems (ToP), a simpler version of ToT, which we hypothesise can work better for complex tasks that can be divided into identical subtasks. Our empirical results show that our approach outperforms ToT and GoT, and in addition performs better than CoT on complex reasoning tasks. All code for this paper is publicly available here: https://github.com/ArmelRandy/tree-of-problems.<|reference_end|>
@article{zebaze2024tree,
    title={Tree of Problems: Improving structured problem solving with
  compositionality},
    author={Armel Zebaze and Beno^it Sagot and Rachel Bawden},
    journal={arXiv preprint arXiv:2410.06634},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06634},
    primaryClass={cs.CL}
}