2023
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
More tokens and more latency per answer, so it earns its place only where a wrong answer is expensive. We do not reach for it on classification or extraction.
Samples several independent reasoning paths for the same question and takes the most common answer, instead of trusting a single greedy chain. The intuition is that a correct derivation can be reached many ways while a wrong one is usually reached once.
@inproceedings{wang2022selfconsistency,
author = {Xuezhi Wang and Jason Wei and Dale Schuurmans and Quoc Le and Ed Chi and Sharan Narang and Aakanksha Chowdhery and Denny Zhou},
title = {Self-Consistency Improves Chain of Thought Reasoning in Language Models},
booktitle = {International Conference on Learning Representations (ICLR) 2023},
year = {2022},
eprint = {2203.11171},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2203.11171}
}Ejentic did not author this paper. Credit belongs to the authors named above; the note is ours.
2023
More tokens and more latency per answer, so it earns its place only where a wrong answer is expensive. We do not reach for it on classification or extraction.
2023
Cheap, and it works. When an agent fails a task, a written critique plus one retry often beats a bigger model.
2022
The pattern behind most of the agents we build. The trace is not decoration: it is the only way to debug an agent that took a wrong turn three steps ago.