Papers with prompt optimization methods
Local Prompt Optimization (2025.naacl-short)
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| Challenge: | Existing prompt optimization methods optimize prompts globally, but they lack the correct words for a task. |
| Approach: | They propose a local prompt optimization process that integrates with any general automatic prompt engineering method to optimize a prompt over a large vocabulary. |
| Outcome: | The proposed method improves on Math Reasoning and BIG-bench Hard benchmarks and shows that it can converge to the optimal prompt faster than global methods. |
Learning from Contrastive Prompts: An Automated Prompt Optimization Framework (2026.findings-acl)
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| Challenge: | Existing prompt optimization methods often underperform due to learning exclusively from incorrect samples. |
| Approach: | They propose a framework that leverages contrastive prompts to distinguish between high- and low-performing cases. |
| Outcome: | The proposed framework can generalize across open and proprietary models and NLU benchmarks. |
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker (2023.findings-acl)
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| Challenge: | Existing studies suggest re-rankers by fine-tuning pre-trained language models . however, manual search for discrete prompts is expensive and sub-optimal in transferability . |
| Approach: | They propose a discrete prompt optimization method that guides the generated texts toward optimal prompts . they propose to use large-scale language models as a zero-shot re-ranker . |
| Outcome: | The proposed method improves the performance of the re-ranker against baselines and human prompts. |
PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling (2024.emnlp-main)
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| Challenge: | Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task. |
| Approach: | They propose a method to optimize prompts for LLM-driven multi-step tasks using a human-designed feedback rule. |
| Outcome: | The proposed method outperforms human-engineered prompts and several other prompt optimization methods on 11 representative multi-step tasks. |
Self-Supervised Prompt Optimization (2025.findings-emnlp)
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Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Xinbing Liang, Fengwei Teng, Jinhao Tu, Fashen Ren, Xiangru Tang, Sirui Hong, Chenglin Wu, Yuyu Luo
| Challenge: | Existing prompt optimization methods rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain. |
| Approach: | They propose a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without external reference. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt optimization methods with significantly lower costs and fewer samples. |
StraGo: Harnessing Strategic Guidance for Prompt Optimization (2024.findings-emnlp)
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Yurong Wu, Yan Gao, Bin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, Linjun Yang
| Challenge: | Existing methods for prompt optimization often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures. |
| Approach: | They propose a method to mitigate prompt drifting by integrating in-context learning to formulate specific, actionable strategies for prompt optimization. |
| Outcome: | The proposed approach mitigates prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. |
Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models (2026.findings-acl)
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Xudong Wang, Chaoning Zhang, Chenghao Li, Shuxu Chen, Qigan Sun, Jiaquan Zhang, Fachrina Dewi Puspitasari, Tae-Ho Kim, Jiwei Wei, Malu Zhang, Guoqing Wang, Yang Yang, Heng Tao Shen
| Challenge: | Existing automatic prompt optimization methods fail to optimize prompts and decoding hyperparameters within a unified framework to achieve stable global improvements. |
| Approach: | They propose a dynamic prompt optimization framework for complex reasoning that unifies prompt templates and decodes hyperparameters as inheritable agent configurations. |
| Outcome: | Experiments on multiple mathematical and hybrid reasoning benchmarks show that Agent-GWO improves accuracy and stability over existing prompt optimization methods. |
Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs (2025.findings-acl)
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| Challenge: | Existing prompt optimization methods rely on extensive manual effort or meta-cognitive abilities, making them less effective for LwLLMs. |
| Approach: | They propose a direct behavior optimization parameter that transforms the optimization of complex prompts into discrete, quantifiable execution sequences using a gradient-free Monte Carlo Tree Search. |
| Outcome: | The proposed method outperforms current prompt optimization methods on seven challenging tasks where state-of-the-art LLMs excel but LwLLMs generally underperform. |
Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers (2025.findings-acl)
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| Challenge: | Existing prompt optimization methods have found effective prompts, but they often differ from sophisticated prompts carefully designed by human experts. |
| Approach: | They propose to integrate prompt design strategies into prompt optimization by using a Thompson sampling-based approach. |
| Outcome: | The proposed method incorporates prompt design strategies into the prompt optimization process. |
RiOT: Efficient Prompt Refinement with Residual Optimization Tree (2025.acl-long)
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| Challenge: | Existing methods for automatic prompt optimization face two challenges: lack of diversity and semantic drift. |
| Approach: | They propose a framework for automatic prompt optimization that iteratively refines prompts through text gradients and selects the best prompt using perplexity. |
| Outcome: | The proposed framework outperforms existing prompt optimization methods and manual prompting on commonsense, mathematical, logical, temporal, and semantic reasoning benchmarks. |
ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities (2025.emnlp-main)
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| Challenge: | Existing research has explored model-driven strategies for prompt optimization, but these methods suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability. |
| Approach: | They propose a framework that optimizes and generates role-playing prompts by limiting the prompt search space to role-player scenarios. |
| Outcome: | The proposed framework matches and surpasses existing prompt optimization methods in terms of performance. |