Why Prompt Optimization Works, and Why It Sometimes Doesn't: A Causal-Inspired Edit-Level Analysis
arXiv:2605.26655v1 Announce Type: cross Abstract: Automated prompt optimization methods (e.g., DSpy, TextGrad) can substantially improve the performance of large language model (LLM), however, their generalization ability across different tasks remains underperformed. In practice, the superiority of the optimized prompt on one benchmark often fails to transfer to another, and this limitation persists even when switching across different LLM backbones. To investigate the underexplored sources of heterogeneity in prompt performance, we conduct a causal inference-inspired observational analysis o
The rapid development and widespread adoption of Large Language Models (LLMs) have exposed critical limitations in their practical deployment, particularly concerning prompt generalization and optimization.
Understanding the mechanisms behind prompt optimization's successes and failures is crucial for developing robust, transferable, and reliable AI systems, impacting their industrial application and scalability.
This research provides a deeper, causal-inspired understanding of prompt engineering, moving it from largely empirical to more theoretically grounded, enabling more systematic improvements in LLM performance across tasks.
- · AI researchers
- · prompt engineering platforms
- · enterprises deploying LLMs
- · LLM developers relying on ad-hoc prompt tuning
- · companies with non-generalizable AI solutions
Improved understanding leads to more effective and generalizable prompt optimization techniques for LLMs.
Enhanced LLM performance across diverse tasks reduces development costs and accelerates AI integration into various industries.
More reliable AI systems enable the automation of highly complex white-collar workflows, leading to significant productivity gains and job displacement in specific sectors.
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Read at arXiv cs.LG