标题： 评估用于小语言模型、提示和评估指标的进化搜索引擎 显示英文标题

标题： Assessing an evolutionary search engine for small language models, prompts, and evaluation metrics

摘要： 并发优化语言模型和教学提示对于部署高效且有效的AI系统提出了重大挑战，特别是在平衡性能与计算成本（如标记使用量）时。 本文介绍并评估了一种双目标进化搜索引擎，旨在探索这一复杂空间，特别关注小型语言模型（SLMs）。 我们采用NSGA-II算法和提示语法，在一些推理任务中同时优化任务准确性和标记效率。 我们的结果成功识别出多样化的高性能模型-提示组合，定量揭示了两个目标之间的关键权衡。 这项研究突显了特定SLMs与提示结构（例如，指令、上下文、思维链）之间的任务特定亲和性。 生成的实用帕累托前沿为决策者提供了一组可适应其特定约束的优化解决方案。 这种自动化方法超越了传统的手动调整，为发现有效的人机交互模式提供了基础框架。 显示英文摘要

摘要： The concurrent optimization of language models and instructional prompts presents a significant challenge for deploying efficient and effective AI systems, particularly when balancing performance against computational costs like token usage. This paper introduces and assesses a bi-objective evolutionary search engine designed to navigate this complex space, focusing specifically on Small Language Models (SLMs). We employ the NSGA-II algorithm and prompt grammar to simultaneously optimize for task accuracy and token efficiency across some reasoning tasks. Our results successfully identify diverse, high-performing model-prompt combinations, quantitatively revealing the critical trade-off between the two objectives. This research highlights task-specific affinities between particular SLMs and prompt structures (e.g., instructions, context, chain of thought). The generated practical Pareto fronts offer decision-makers a portfolio of optimized solutions adaptable to their specific constraints. This automated approach moves beyond traditional manual tuning, providing a foundational framework for discovering effective human-AI interaction patterns.