ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities

• URL: http://arxiv.org/abs/2506.02480v1
• Date: Tue, 03 Jun 2025 05:51:35 GMT
• Title: ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities
• Authors: Yifan Duan, Yihong Tang, Kehai Chen, Liqiang Nie, Min Zhang,
• Abstract summary: High-quality prompts are crucial for eliciting outstanding performance from large language models on complex tasks.<n>We propose ORPP, a framework that enhances model performance by optimizing and generating role-playing prompts.<n>We show that ORPP not only matches but in most cases surpasses existing mainstream prompt optimization methods in terms of performance.
• Score: 64.24517317344959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality prompts are crucial for eliciting outstanding performance from large language models (LLMs) on complex tasks. Existing research has explored model-driven strategies for prompt optimization. However, these methods often suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability.To address these challenges, we propose ORPP (Optimized Role-Playing Prompt),a framework that enhances model performance by optimizing and generating role-playing prompts. The core idea of ORPP is to confine the prompt search space to role-playing scenarios, thereby fully activating the model's intrinsic capabilities through carefully crafted, high-quality role-playing prompts. Specifically, ORPP first performs iterative optimization on a small subset of training samples to generate high-quality role-playing prompts. Then, leveraging the model's few-shot learning capability, it transfers the optimization experience to efficiently generate suitable prompts for the remaining samples.Our experimental results show that ORPP not only matches but in most cases surpasses existing mainstream prompt optimization methods in terms of performance. Notably, ORPP demonstrates superior "plug-and-play" capability. In most cases, it can be integrated with various other prompt methods and further enhance their effectiveness.

Related papers

• Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models? [62.579951798437115]
  This work investigates iterative approximate evaluation for arbitrary prompts.<n>It introduces Model Predictive Prompt Selection (MoPPS), a Bayesian risk-predictive framework.<n>MoPPS reliably predicts prompt difficulty and accelerates training with significantly reduced rollouts.
  arXiv  Detail & Related papers  (2025-07-07T03:20:52Z)
• Rethinking Prompt Optimizers: From Prompt Merits to Optimization [14.01541576309104]
  We introduce MePO, a merit-guided, lightweight, locally deployable prompt training dataset built from merit-aligned prompts.<n>MePO avoids online optimization, reduces cost and privacy concerns, and, by learning clear, interpretable merits, generalizes effectively to both large-scale and lightweight inference models.
  arXiv  Detail & Related papers  (2025-05-15T03:31:37Z)
• SPRIG: Improving Large Language Model Performance by System Prompt Optimization [45.96513122345295]
  Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends on the choice of prompt. We propose SPRIG, an edit-based genetic algorithm that iteratively constructs prompts from prespecified components to maximize the model's performance in general scenarios. We evaluate the performance of system prompts on a collection of 47 different types of tasks to ensure generalizability.
  arXiv  Detail & Related papers  (2024-10-18T18:51:44Z)
• In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement [71.60563181678323]
  Large language models (LLMs) have achieved great success across diverse tasks, and fine-tuning is sometimes needed to further enhance generation quality.<n>To handle these challenges, a direct solution is to generate high-confidence'' data from unsupervised downstream tasks.<n>We propose a novel approach, pseudo-supervised demonstrations aligned prompt optimization (PAPO) algorithm, which jointly refines both the prompt and the overall pseudo-supervision.
  arXiv  Detail & Related papers  (2024-10-04T03:39:28Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries.<n>We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks.<n> Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• Large Language Models Prompting With Episodic Memory [53.8690170372303]
  We propose PrOmpting with Episodic Memory (POEM), a novel prompt optimization technique that is simple, efficient, and demonstrates strong generalization capabilities. In the testing phase, we optimize the sequence of examples for each test query by selecting the sequence that yields the highest total rewards from the top-k most similar training examples in the episodic memory. Our results show that POEM outperforms recent techniques like TEMPERA and RLPrompt by over 5.3% in various text classification tasks.
  arXiv  Detail & Related papers  (2024-08-14T11:19:28Z)
• Localized Zeroth-Order Prompt Optimization [54.964765668688806]
  We propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO) ZOPO incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency.
  arXiv  Detail & Related papers  (2024-03-05T14:18:15Z)
• FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema [36.65009632307124]
  We propose Free-from Instruction-oriented Prompt Optimization (FIPO) to improve task performance of large language models (LLMs)<n>FIPO uses a modular APO template that dynamically integrate the naive task instruction, optional instruction responses, and optional ground truth to produce finely optimized prompts.<n>We validate FIPO framework across five public benchmarks and six testing models.
  arXiv  Detail & Related papers  (2024-02-19T03:56:44Z)
• PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models [9.362082187605356]
  We present PhaseEvo, an efficient automatic prompt optimization framework that combines the generative capability of LLMs with the global search proficiency of evolution algorithms. PhaseEvo significantly outperforms the state-of-the-art baseline methods by a large margin whilst maintaining good efficiency.
  arXiv  Detail & Related papers  (2024-02-17T17:47:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.