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.
Shows that asking a model to write out intermediate reasoning steps before answering substantially improves multi-step arithmetic, commonsense and symbolic reasoning, and that the behaviour appears without any fine-tuning at sufficient model scale.
@inproceedings{wei2022chainofthought,
author = {Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed Chi and Quoc Le and Denny Zhou},
title = {Chain-of-Thought Prompting Elicits Reasoning in Large Language Models},
booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)},
year = {2022},
eprint = {2201.11903},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2201.11903}
}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.