The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Demystifying Prompts in Language Models via Perplexity Estimation
Open accessAbstract
Language models can be prompted to perform a wide variety of tasks with zero-and few-shot incontext learning.However, performance varies significantly with the choice of prompt, and we do not yet understand why this happens.In this paper, we analyze the factors that contribute to this variance and establish a new empirical hypothesis: the performance of a prompt is predicted by the extent to which the model is familiar with the language it contains.Over a wide range of tasks, we show that the lower the perplexity of the prompt, the better it is able to perform the task, when considering reasonable prompts that are related to it.As part of our analysis, we also devise a method to automatically extend a small seed set of manually written prompts by paraphrasing with GPT3 and backtranslation.This larger set allows us to verify that perplexity is a strong predictor of the success of a prompt and we show that the lowest perplexity prompts are consistently effective.
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“This provides a clear signal for detection methods like Perplexity (Gonen et al., 2023) and Min-K% Prob (Shi et al., 2024), which are built upon this likelihood principle.”
From Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models (2025) · cites this paper as Gonen et al., 2023
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Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Character-level Convolutional Networks for Text ClassificationPreprint
Zhang, X., Zhao, J., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.01626 · Cited by 4,481
DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia
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Learning Word Vectors for Sentiment Analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,710
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473
No Language Left Behind: Scaling Human-Centered Machine TranslationPreprint
Costa-jussà, M., Cross, J., Çelebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2207.04672 · Cited by 2,176
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Making Pre-trained Language Models Better Few-shot Learners
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Sorted by citations
A Survey on In-context Learning
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.64 · Cited by 649
Security and Privacy Challenges of Large Language Models: A Survey
Das, B. C., Amini, M. H., & Wu, Y. (2025) · ACM Computing Surveys · doi:10.1145/3712001 · Cited by 445
Gao, Y., Li, R., Croxford, E., Caskey, J., Patterson, B. W., Churpek, M. M., Miller, T. A., Dligach, D., & Afshar, M. (2024) · JMIR AI · doi:10.2196/58670 · Cited by 169
State of What Art? A Call for Multi-Prompt LLM Evaluation
Mizrahi, M., Kaplan, G., Malkin, D., Dror, R., Shahaf, D., & Stanovsky, G. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00681 · Cited by 113
Prompting meaning: a hermeneutic approach to optimising prompt engineering with ChatGPT
Henrickson, L., & Meroño-Peñuela, A. (2023) · AI & Society · doi:10.1007/s00146-023-01752-8 · Cited by 136
Metadata
- Authors
- Gonen, H., Iyer, S., Blevins, T., Smith, N., & Zettlemoyer, L.
- Affiliations
- University of Washington (US)
- Allen Institute for Artificial Intelligence (US)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Published
- 2023
- Pages
- 10136-10148
- OpenAlex
- W4389520255
- Cited by
- 109
- References
- 32
- Citations by year
- 2026: 14 · 2025: 33 · 2024: 30 · 2023: 31
- FWCI
- 19.14
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- prompt engineering, language models, in-context learning, zero-shot learning, few-shot learning, paraphrase, back-translation
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