TAPO: Task-Referenced Adaptation for Prompt Optimization
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Abstract (arXiv)
Prompt engineering can significantly improve the performance of large language models (LLMs), with automated prompt optimization (APO) gaining significant attention due to the time-consuming and laborious nature of manual prompt design. However, much of the existing work in APO overlooks task-specific characteristics, resulting in prompts that lack domain specificity and are not well-suited for task-specific optimization. In this paper, we introduce TAPO, a multitask-aware prompt optimization framework composed of three key modules. First, a task-aware metric selection module is proposed to enhance task-specific prompt generation capabilities. Second, we present a multi-metrics evaluation module to jointly evaluate prompts from multiple perspectives. Third, an evolution-based optimization framework is introduced for automatic prompt refinement, which improves adaptability across various tasks. Extensive experiments on six datasets demonstrate the effectiveness of our approach, and our code is publicly available.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 29 of its 38 references that are in the map. First posted on arXiv 12 Jan 2025.
Builds on
29 of 38 references.
References · 29
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
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- 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
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- 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 551
- 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
17 more
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- 2023When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applicationscited by 111
- 2023Eliciting Human Preferences with Language Modelscited by 92
- 2023Prompt Engineering a Prompt Engineercited by 53
- 2022TEMPERA: Test-Time Prompting via Reinforcement Learningcited by 50
- 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
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- 2024G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Modelscited by 34
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- 2024Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directionscited by 12
- 2024Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmarkcited by 3
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.