Benoît Sagot, Rachel Bawden, A. Zebaze
We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| masoudhashemi/AWMS | pwc_unofficial | 3 of 3 |
| ArmelRandy/tree-of-problems | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| extract_boxed | Ran | masoudhashemi/AWMS/awms/utils.py code served (permissive licence) · get_code("432af6a5e4dafb42") |
| latex_to_sympy_matrix | Ran | masoudhashemi/AWMS/awms/utils.py code served (permissive licence) · get_code("725bdea2d7c60809") |
| parse_intervals | Ran | masoudhashemi/AWMS/awms/utils.py code served (permissive licence) · get_code("16cb466e83af607e") |
| divide | Not yet run | ArmelRandy/tree-of-problems/top/divide.py code served (permissive licence) · get_code("d945e94523780ec5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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. Q: "Eric, Shaun, Christopher, Wooh" A: Q: If you follow these instructions, what are the coordinates of the end point if you start at the point (0, 0), facing the positive y-axis? Turn left. Turn around. Turn left. Take 7 steps. Take 2 steps. Take 4 steps. Take 8 steps. Let's think step by step. We start at the point (0, 0), facing the positive y-axis. (1) Turn left: (0, 0), facing the negative x-axis. (2) Turn around: (0,0), facing the positive x-axis. (3) Turn left: (0, 0), facing the positive y-axis. (4) Take 7 steps: (0, 7) facing the positive y-axis. So the answer is (0, 7).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.06634")
get_code_for_paper("2410.06634")
have("2410.06634")
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