---
title: "An Information-theoretic Approach to Prompt Engineering"
description: "Sorensen et al. propose a label-free alternative that needs only the model's output probabilities. 38 references, cited by 69 papers here."
authors: ["Taylor Sorensen", "Joshua Robinson", "Christopher Rytting", "Alexander Shaw", "Kyle Rogers", "Alexia Delorey", "Mahmoud Khalil", "Nancy Fulda", "David Wingate"]
year: 2022
venue: "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2022.acl-long.60/"
doi: "10.18653/v1/2022.acl-long.60"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sorensen-2022-information-theoretic-approach-prompt-engineering/"
---
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# An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels

**Authors:** [Taylor Sorensen](https://md.research.elamur.ai/free/en/authors/taylor-sorensen/), [Joshua Robinson](https://md.research.elamur.ai/free/en/authors/joshua-robinson/), [Christopher Rytting](https://md.research.elamur.ai/free/en/authors/christopher-michael-rytting/), Alexander Shaw, Kyle Rogers, Alexia Delorey, [Mahmoud Khalil](https://md.research.elamur.ai/free/en/authors/mahmoud-khalil/), [Nancy Fulda](https://md.research.elamur.ai/free/en/authors/nancy-fulda/) and [David Wingate](https://md.research.elamur.ai/free/en/authors/david-wingate/) · **Year:** 2022 · **Venue:** [Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2022](https://md.research.elamur.ai/free/en/venues/acl/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.acl-long.60/) · [PDF](https://aclanthology.org/2022.acl-long.60.pdf) · [DOI 10.18653/v1/2022.acl-long.60](https://doi.org/10.18653/v1/2022.acl-long.60) · [OpenAlex](https://openalex.org/W4285129823)

**Citations:** 109 citations (OpenAlex, 2026-10-14) · [69](https://md.research.elamur.ai/free/en/papers/sorensen-2022-information-theoretic-approach-prompt-engineering/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Large language models are highly sensitive to how a prompt is worded. Picking a good template usually requires a labeled validation set, or gradient access to the model for continuous prompt tuning. Sorensen et al. propose a label-free alternative that needs only the model's output probabilities.

For each candidate template, they estimate the mutual information (MI) between the inputs and the model's answer distribution, then keep the template with the highest MI. Tasks are framed as One-token Response (OTR) problems: the probability mass on tokens that start each answer option is pooled and normalized into a distribution over answers.

The authors wrote 20 templates for each of 8 datasets covering 7 NLP tasks and tested models ranging from GPT-2 to GPT-3 175B. MI correlated strongly with test accuracy, especially on the biggest models.

This matters for low-resource settings such as Ukrainian NLP. When labels are scarce and a model is reachable only through an inference API, MI still offers a cheap, principled way to choose a prompt.

## Key points
- On GPT-3 Davinci (175B), the MI-chosen template closes about 90% of the gap between average and best template accuracy, and it picks the single best template on 3 of 8 datasets.
- The benchmarks include open-ended sets (SQuAD, LAMBADA, ROCStories) as well as classification-style sets such as BoolQ, COPA, WiC and IMDB. Other models tested include GPT-J 6B, GPT-Neo 2.7B and GPT-2 (124M, 1.5B).
- Ensembling the 5 highest-MI templates matches or beats ensembling all 20 in every case but one, at roughly a quarter of the inference cost.
- Templates selected on one model usually transfer well to another: only 1 of 64 selection/inference model pairs had a negative average gain.
- Code is available at github.com/BYU-PCCL/information-theoretic-prompts.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Brigham Young University](https://md.research.elamur.ai/free/en/universities/brigham-young-university/) — University, Provo, United States · 15 works · 40,065 citations

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## Cite

Cite as: Taylor Sorensen, Joshua Robinson, Christopher Rytting, Alexander Shaw, Kyle Rogers, Alexia Delorey, Mahmoud Khalil, Nancy Fulda and David Wingate. 2022. An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sorensen2022information,
  title = {{An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels}},
  author = {Taylor Sorensen, Joshua Robinson, Christopher Rytting, Alexander Shaw, Kyle Rogers, Alexia Delorey, Mahmoud Khalil, Nancy Fulda and David Wingate},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.acl-long.60},
  url = {https://doi.org/10.18653/v1/2022.acl-long.60}
}
```

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*Updated 24 October 2026*