# Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection

arXiv: https://arxiv.org/abs/2411.07446 · HTML: https://mlsanity.com/paper/2411.07446/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 20 of 20)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Improving Text-to-Image Consistency via Automatic Prompt Optimization](https://mlsanity.com/paper/2403.17804/) (2024)
- [ETHOS: an Online Hate Speech Detection Dataset](https://mlsanity.com/paper/2006.08328/) (2020)
- [Are Large Language Models Good Prompt Optimizers?](https://mlsanity.com/paper/2402.02101/) (2024)
- [Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers](https://mlsanity.com/paper/2402.17564/) (2024)
- [Agent Instructs Large Language Models to be General Zero-Shot Reasoners](https://mlsanity.com/paper/2310.03710/) (2023)
- [PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling](https://mlsanity.com/paper/2402.08702/) (2024)
- [Task Facet Learning: A Structured Approach to Prompt Optimization](https://mlsanity.com/paper/2406.10504/) (2024)
- [VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization](https://mlsanity.com/paper/2402.07398/) (2024)
- [SPELL: Semantic Prompt Evolution based on a LLM](https://mlsanity.com/paper/2310.01260/) (2023)
- [Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing](https://mlsanity.com/paper/2310.13855/) (2023)
- [GRAD-SUM: Leveraging Gradient Summarization for Optimal Prompt Engineering](https://mlsanity.com/paper/2407.12865/) (2024)

## Built on by (most cited first, 18 of 18)

- [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://mlsanity.com/paper/2507.21046/) (2025)
- [Self-Supervised Prompt Optimization](https://mlsanity.com/paper/2502.06855/) (2025)
- [MEMTRACK: Evaluating Long-Term Memory and State Tracking in Multi-Platform Dynamic Agent Environments](https://mlsanity.com/paper/2510.01353/) (2025)
- [Brief analysis of DeepSeek R1 and its implications for Generative AI](https://mlsanity.com/paper/2502.02523/) (2025)
- [A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm](https://mlsanity.com/paper/2502.18746/) (2025)
- [Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings](https://mlsanity.com/paper/2506.00178/) (2025)
- [Your Code Agent Can Grow Alongside You with Structured Memory](https://mlsanity.com/paper/2603.13258/) (2026)
- [PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models](https://mlsanity.com/paper/2505.16307/) (2025)
- [SkillAdam: Stable and Efficient Skill Evolution for Agents](https://mlsanity.com/paper/2609.08944/) (2026)
- [UniAPO: Unified Multimodal Automated Prompt Optimization](https://mlsanity.com/paper/2508.17890/) (2025)
- [UPA: Unsupervised Prompt Agent via Tree-Based Search and Selection](https://mlsanity.com/paper/2601.23273/) (2026)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)
- [ELPO: Ensemble Learning Based Prompt Optimization for Large Language Models](https://mlsanity.com/paper/2511.16122/) (2025)
- [Concise Geometric Description as a Bridge: Unleashing the Potential of LLM for Plane Geometry Problem Solving](https://mlsanity.com/paper/2601.21164/) (2026)
- [C-MOP: Integrating Momentum and Boundary-Aware Clustering for Enhanced Prompt Evolution](https://mlsanity.com/paper/2602.10874/) (2026)
- [Reflective Context Learning: Studying the Optimization Primitives of Context Space](https://mlsanity.com/paper/2604.03189/) (2026)
- [OmniPhys: Knowledge-Graph-Driven Benchmarking and Collective Optimization for Physical Commonsense in Text-to-Image Generation](https://mlsanity.com/paper/2607.25641/) (2026)
- [Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations](https://mlsanity.com/paper/2610.00928/) (2026)

## Abstract (arXiv)

Automatic prompt engineering aims to enhance the generation quality of large language models (LLMs). Recent works utilize feedbacks generated from erroneous cases to guide the prompt optimization. During inference, they may further retrieve several semantically-related exemplars and concatenate them to the optimized prompts to improve the performance. However, those works only utilize the feedback at the current step, ignoring historical and unseleccted feedbacks which are potentially beneficial. Moreover, the selection of exemplars only considers the general semantic relationship and may not be optimal in terms of task performance and matching with the optimized prompt. In this work, we propose an Exemplar-Guided Reflection with Memory mechanism (ERM) to realize more efficient and accurate prompt optimization. Specifically, we design an exemplar-guided reflection mechanism where the feedback generation is additionally guided by the generated exemplars. We further build two kinds of memory to fully utilize the historical feedback information and support more effective exemplar retrieval. Empirical evaluations show our method surpasses previous state-of-the-arts with less optimization steps, i.e., improving F1 score by 10.1 on LIAR dataset, and reducing half of the optimization steps on ProTeGi.