arXiv Computation and Language
Projection-Free Evolution Strategies for Continuous Prompt Search
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Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality
The paper investigates how small lexical changes in prompts can cause large performance swings in large language models. Using a dataset of 132,000 prompt variants, the authors uncover a scaling law linking higher average task performance to lower variance and greater robustness. They identify domain-specific terminology and explicit action directives as key linguistic factors that stabilize prompts, and propose an automated Prompt-Refining Agent that reduces performance variance by 40.7% in code generation while maintaining or improving mean performance.
arXiv AI
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Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models
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GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer
GFlowPO introduces a probabilistic framework for optimizing language model prompts by treating prompt search as posterior inference over latent prompts, regularized by a meta-prompted reference-LM prior. The method first fine‑tunes a lightweight prompt‑LM using an off‑policy Generative Flow Network objective that reuses past prompt evaluations for sample‑efficient exploration. It then applies a training‑free Dynamic Memory Update to refine the meta‑prompt with diverse and top‑performing prompts, leading to consistent improvements over recent discrete prompt optimization baselines on few‑shot classification, instruction induction, and question‑answering tasks.
arXiv AI
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Budgeted Prompt-Set Expansion and Refinement for Language Models via Simulation Optimization
The paper proposes a two‑stage framework for expanding and refining a set of prompts for language models under a limited evaluation budget. In the first stage, it generates a finite candidate set of prompts represented by moderate‑dimensional vectors. The second stage builds a surrogate model of prompt scores and sequentially selects prompts for evaluation, proving consistency of this procedure and introducing a refinement step that searches beyond the initial candidates. Numerical experiments show the method outperforms baseline prompt selection, especially when evaluation calls are constrained.
arXiv Computation and Language
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AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization
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arXiv Machine Learning
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Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation
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