title: Tree of Problems: Improving structured problem solving with
    compositionality
summary: 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.
paper_authors._id: 670e05ef1cc9c1fdf0a093ff
paper_authors.name: Armel Zebaze
paper_authors._id: 670e05ef1cc9c1fdf0a09400
paper_authors.name: Benoît Sagot
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-15T16:32:56.867Z
paper_authors._id: 602ba2a739515f8d31237967
paper_authors.avatarUrl: /avatars/ef534712ca682bf74ec7eef17d3d1b5f.svg
paper_authors.fullname: Benoît Sagot
paper_authors.type: user
paper_authors.user: sagot
paper_authors._id: 670e05ef1cc9c1fdf0a09401
paper_authors.name: Rachel Bawden
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-15T16:33:02.609Z
paper_authors._id: 613a0c85e63aa832a98cecc4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1653912785529-613a0c85e63aa832a98cecc4.png
paper_authors.fullname: Rachel Bawden
paper_authors.type: user
paper_authors.user: rbawden
paper_title: Tree of Problems: Improving structured problem solving with
    compositionality
paper_summary: 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.
paper_ai_summary: A new method, Tree of Problems, improves upon Chain-of-Thought, Tree of Thoughts, and Graph of Thoughts for complex reasoning tasks by simplifying the division of identical subtasks.
paper_ai_keywords: Chain-of-Thought Tree of Thoughts Graph of Thoughts Tree of Problems complex reasoning tasks identical subtasks