mlsanityData to 14 Oct 2026

promptolution: A Unified, Modular Framework for Prompt Optimization

arXiv 2512.02840 ↗First posted 2 Dec 2025cs.CLcited by 6

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Abstract (arXiv)

Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers demonstrate its effectiveness, practical adoption is hindered because existing implementations are often tied to unmaintained, isolated research codebases or require invasive integration into application frameworks. To address this, we introduce promptolution, a unified, modular open-source framework that provides all components required for prompt optimization within a single extensible system for both practitioners and researchers. It integrates multiple contemporary discrete prompt optimizers, supports systematic and reproducible benchmarking, and returns framework-agnostic prompt strings, enabling seamless integration into existing LLM pipelines while remaining agnostic to the underlying model implementation.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 24 of its 51 references that are in the map. First posted on arXiv 2 Dec 2025.

Builds on

24 of 51 references.

References · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  6. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  7. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  8. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  9. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,392
  10. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  11. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  12. 2019How Can We Know What Language Models Know?cited by 959
12 more
  1. 2023Large Language Models as Optimizerscited by 864
  2. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  3. 2025GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningcited by 542
  4. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
  5. 2024Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programscited by 291
  6. 2021OpenPrompt: An Open-source Framework for Prompt-learningcited by 148
  7. 2023Black-Box Prompt Optimization: Aligning Large Language Models without Model Trainingcited by 122
  8. 2023TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Taskscited by 49
  9. 2025A Survey of Automatic Prompt Engineering: An Optimization Perspectivecited by 41
  10. 2024Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Togethercited by 40
  11. 2024PromptWizard: Task-Aware Prompt Optimization Frameworkcited by 36
  12. 2025Promptomatix: An Automatic Prompt Optimization Framework for Large Language Modelscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.