mlsanityData to 14 Oct 2026

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

arXiv 2407.04118 ↗First posted 4 Jul 2024cs.CL cs.AIcited by 21

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Abstract (arXiv)

Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. However, a good prompt is not solely defined by its wording, but also binds to the nature of the LLM in question. In this work, we first quantitatively demonstrate that different prompts should be adapted to different LLMs to enhance their capabilities across various downstream tasks in NLP. Then we novelly propose a model-adaptive prompt optimizer (MAPO) method that optimizes the original prompts for each specific LLM in downstream tasks. Extensive experiments indicate that the proposed method can effectively refine prompts for an LLM, leading to significant improvements over various downstream tasks.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 33 of its 66 references that are in the map. First posted on arXiv 4 Jul 2024.

Builds on

33 of 66 references.

References · 33

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2017Proximal Policy Optimization Algorithmscited by 15,215
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  7. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
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  9. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
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21 more
  1. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,729
  2. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  3. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  4. 2019How Can We Know What Language Models Know?cited by 959
  5. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
  6. 2021Cross-Task Generalization via Natural Language Crowdsourcing Instructionscited by 699
  7. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 653
  8. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  9. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 463
  10. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
  11. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
  12. 2021Factual Probing Is [MASK]: Learning vs. Learning to Recallcited by 291
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  14. 2022Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimizationcited by 246
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  17. 2018Transforming Question Answering Datasets Into Natural Language Inference Datasetscited by 99
  18. 2023Guiding Large Language Models via Directional Stimulus Promptingcited by 95
  19. 2024Task-Agnostic Detector for Insertion-Based Backdoor Attackscited by 23
  20. 2022Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanationcited by 16
  21. 2023Black-box Prompt Tuning with Subspace Learningcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 7; 2026: 6.

21 papers.

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  1. 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptionscited by 17
  2. 2024NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-trainingcited by 15
  3. 2024XMeCap: Meme Caption Generation with Sub-Image Adaptabilitycited by 14
  4. 2024Recent Advancement of Emotion Cognition in Large Language Modelscited by 9
  5. 2025PromptPex: Automatic Test Generation for Language Model Promptscited by 9
  6. 2024HOTVCOM: Generating Buzzworthy Comments for Videoscited by 7
  7. 2025ZERA: Zero-init Instruction Evolving Refinement Agent -- From Zero Instructions to Structured Prompts via Principle-based Optimizationcited by 6
  8. 2025A Survey on AgentOps: Categorization, Challenges, and Future Directionscited by 4
  9. 2026Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agentscited by 3
  10. 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Modelscited by 2
  11. 2024From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmapcited by 2
  12. 2025Prompt and Parameter Co-Optimization for Large Language Modelscited by 2
9 more
  1. 2025Maps for Autonomous Driving: Full-process Survey and Frontierscited by 1
  2. 2026When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systemscited by 1
  3. 2026Prompt-Model Interaction Reaches the Fixed Points: A deterministic, task-free structural readout -- and the factorizations of it that failedcited by 1
  4. 2024Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)cited by 0
  5. 2024Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)cited by 0
  6. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
  7. 2025Noise or Nuance: An Investigation Into Useful Information and Filtering For LLM Driven AKBCcited by 0
  8. 2026Evaluating Prompt Engineering Strategies for Sentiment Control in AI-Generated Textscited by 0
  9. 2026MoPrO-SD: Modular Prompt Optimization for Stance Detectioncited by 0