mlsanityData to 14 Oct 2026

A Toolbox for Improving Evolutionary Prompt Search

arXiv 2511.05120 ↗First posted 7 Nov 2025cs.CLcited by 3

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Abstract (arXiv)

Evolutionary prompt optimization has demonstrated effectiveness in refining prompts for LLMs. However, existing approaches lack robust operators and efficient evaluation mechanisms. In this work, we propose several key improvements to evolutionary prompt optimization that can partially generalize to prompt optimization in general: 1) decomposing evolution into distinct steps to enhance the evolution and its control, 2) introducing an LLM-based judge to verify the evolutions, 3) integrating human feedback to refine the evolutionary operator, and 4) developing more efficient evaluation strategies that maintain performance while reducing computational overhead. Our approach improves both optimization quality and efficiency. We release our code, enabling prompt optimization on new tasks and facilitating further research in this area.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 7 Nov 2025.

Builds on

25 of 43 references.

References · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  6. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  8. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  9. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  10. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  11. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
  12. 2021Cross-Task Generalization via Natural Language Crowdsourcing Instructionscited by 699
13 more
  1. 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 535
  2. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
  3. 2024Reinforcement learningcited by 247
  4. 2023Can large language models provide useful feedback on research papers? A large-scale empirical analysiscited by 200
  5. 2019The Evolved Transformercited by 180
  6. 2023EvoPrompting: Language Models for Code-Level Neural Architecture Searchcited by 131
  7. 2023Language Model Crossover: Variation through Few-Shot Promptingcited by 122
  8. 2021Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learnerscited by 115
  9. 2020ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformationscited by 101
  10. 2022Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?cited by 49
  11. 2023Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistancecited by 33
  12. 2024Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysiscited by 33
  13. 2023Deliberate then Generate: Enhanced Prompting Framework for Text Generationcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMscited by 6
  2. 2026MOSAIC: Adversarial Co-evolution of Specialist Heuristics and Problem Instances for LLM-based Automated Heuristic Designcited by 0
  3. 2026ESPO: Error-Structured Prompt Optimization via Diagnose, Diversify, and Stabilizecited by 0