# Prompt Engineering and Calibration for Zero-Shot Commonsense Reasoning

arXiv: https://arxiv.org/abs/2304.06962 · HTML: https://mlsanity.com/paper/2304.06962/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 0 of its 24 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Built on by (most cited first, 2 of 2)

- [Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index](https://mlsanity.com/paper/2412.01690/) (2024)
- [On Fusing ChatGPT and Ensemble Learning in Discon-tinuous Named Entity Recognition in Health Corpora](https://mlsanity.com/paper/2412.16976/) (2024)

## Abstract (arXiv)

Prompt engineering and calibration make large language models excel at reasoning tasks, including multiple choice commonsense reasoning. From a practical perspective, we investigate and evaluate these strategies on smaller language models. Through experiments on five commonsense reasoning benchmarks, we find that each strategy favors certain models, but their joint effects are mostly negative.