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Automatic Prompt Optimization with “Gradient Descent” and Beam Search

Summary

Writing prompts for LLMs is still mostly manual trial and error. Many existing automatic methods need access to model internals, or they search the prompt space without a clear direction. The authors introduce ProTeGi (Prompt Optimization with Textual Gradients), a nonparametric method that needs only labelled training examples and a black-box LLM API. It imitates gradient descent in text. The LLM examines a prompt's errors on a minibatch and writes a critique, which serves as the textual "gradient". A second prompt then rewrites the original to fix the problems the critique describes, and paraphrasing adds more candidates. These candidates feed a beam search. Deciding which candidates survive is treated as a best arm identification problem and solved with bandit algorithms to limit evaluation cost. Across three benchmark NLP classification tasks and a new LLM jailbreak detection task, ProTeGi improved starting prompts by up to 31%. It also beat earlier prompt-editing baselines by 4–8% on average while using fewer API calls. This matters because it turns vague task descriptions into precise, readable instructions without model training or hyperparameter tuning.

Key points

  • Core loop: a feedback prompt (∇) summarizes the current prompt's flaws from its minibatch errors. An editing prompt (δ) produces improved versions, and a paraphrasing step (LLM_mc) explores nearby wordings.
  • Candidate selection: the paper considers UCB, UCB-E and Successive Rejects bandits. Successive Rejects is provably optimal for best arm identification and needs no hyperparameters.
  • Evaluation: four classification tasks, including Ethos (hate speech) and the newly proposed jailbreak detection problem. Baselines include Monte Carlo (MC) paraphrase search and an RL-based prompt editor.
  • Results: up to 31% gain over the initial prompt and an average 4–8% gain over state-of-the-art prompt learning baselines, with fewer LLM API calls.
  • Limitations and resources:
    • API rate limits and the many calls required can push runtime past an hour.
    • Testing was limited to classification tasks.
    • Code and data are available at github.com/microsoft/LMOps (prompt_optimization).

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

Fields: Natural language processing

Cited by

210 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 204 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.emnlp-main.494

@inproceedings{pryzant2023automatic,
  title = {{Automatic Prompt Optimization with “Gradient Descent” and Beam Search}},
  author = {Pryzant, Reid and Iter, Dan and Li, Jerry and Lee, Yin and Zhu, Chenguang and Zeng, Michael},
  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
  year = {2023},
  doi = {10.18653/v1/2023.emnlp-main.494},
  url = {https://doi.org/10.18653/v1/2023.emnlp-main.494}
}

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