{"slug": "beyond-prompt-engineering-a-systematic-analysis-of-prompt-lexical-sensitivity-on", "title": "Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality", "summary": "A new arXiv study (2608.20349v1) presents the first large-scale, n-gram token-level mechanistic analysis of prompt stability, using 132,000 prompt variants, and finds a Scaling Law of Prompt Performance Stability: higher average task performance correlates with lower variance and greater robustness. The researchers identify Domain-Specific Terminology and Explicit Action Directives as key linguistic drivers, and their automated Prompt-Refining Agent reduces performance variance by 40.7% in code generation tasks while preserving or improving mean performance.", "body_md": "arXiv:2608.20349v1 Announce Type: new\nAbstract: Large Language Models (LLMs) exhibit extreme sensitivity to surface-level prompt variations, in which minor lexical changes can trigger disproportionate performance fluctuations. Moving beyond black-box optimization and coarse-grained templates, we present the first large-scale, n-gram token-level mechanistic analysis of prompt stability, leveraging a dataset of 132,000 prompt variants. Our investigation reveals a fundamental Scaling Law of Prompt Performance Stability: higher average task performance is strongly associated with lower variance and greater robustness across prompt perturbation. We identify two core linguistic drivers underlying this robustness: (1) Domain-Specific Terminology, which tightly anchors semantic boundaries, and (2) Explicit Action Directives, which formalize reasoning trajectories. Together, these elements constrain the model's interpretative space, effectively ``locking in'' more deterministic generation behavior. Building on these insights, we introduce an automated Prompt-Refining Agent that systematically restructures input queries by injecting domain anchoring and operational constraints. Empirical evaluation shows that our approach reduces performance variance by 40.7% in code generation task, while preserving or improving mean performance. These findings provide a statistically grounded and mechanistically interpretable framework for achieving robust prompt engineering.", "url": "https://wpnews.pro/news/beyond-prompt-engineering-a-systematic-analysis-of-prompt-lexical-sensitivity-on", "canonical_source": "https://arxiv.org/abs/2608.20349", "published_at": "2026-08-24 04:00:00+00:00", "updated_at": "2026-08-24 04:14:15.655972+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "natural-language-processing", "ai-research"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/beyond-prompt-engineering-a-systematic-analysis-of-prompt-lexical-sensitivity-on", "markdown": "https://wpnews.pro/news/beyond-prompt-engineering-a-systematic-analysis-of-prompt-lexical-sensitivity-on.md", "text": "https://wpnews.pro/news/beyond-prompt-engineering-a-systematic-analysis-of-prompt-lexical-sensitivity-on.txt", "jsonld": "https://wpnews.pro/news/beyond-prompt-engineering-a-systematic-analysis-of-prompt-lexical-sensitivity-on.jsonld"}}