mlsanityData to 14 Oct 2026

Diverse Prompts: Illuminating the Prompt Space of Large Language Models with MAP-Elites

arXiv 2504.14367 ↗First posted 19 Apr 2025cs.CL cs.AIcited by 1

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Abstract (arXiv)

Prompt engineering is essential for optimizing large language models (LLMs), yet the link between prompt structures and task performance remains underexplored. This work introduces an evolutionary approach that combines context-free grammar (CFG) with the MAP-Elites algorithm to systematically explore the prompt space. Our method prioritizes quality and diversity, generating high-performing and structurally varied prompts while analyzing their alignment with diverse tasks by varying traits such as the number of examples (shots) and reasoning depth. By systematically mapping the phenotypic space, we reveal how structural variations influence LLM performance, offering actionable insights for task-specific and adaptable prompt design. Evaluated on seven BigBench Lite tasks across multiple LLMs, our results underscore the critical interplay of quality and diversity, advancing the effectiveness and versatility of LLMs.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 19 Apr 2025.

Builds on

24 of 38 references.

References · 24

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12 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026PromptHelper: A Prompt Recommender System for Encouraging Creativity in AI Chatbot Interactionscited by 0