arXiv:2605.21515v1 Announce Type: new Abstract: LLM prompting is widely used for naturally stated tasks, yet it is unreliable it may succeed on a few test cases but fail at deployment time. We study performance prediction: given a program, either symbolic (e.g. Python) or a prompt executed on an LLM, and a few in-domain examples, predict its performance on unseen tasks from the same domain. We use a simple coin-flip model, treating each pass/fail program execution as a Bernoulli random variable, whose success probability is the programs unknown performance. In this model, performance depends e
The proliferation of LLM prompting and its inherent unreliability in real-world deployment necessitates robust methods for performance prediction to ensure practical utility and trust.
Reliably predicting LLM performance before wide deployment is crucial for industries adopting AI, enabling more stable, predictable, and trustworthy applications and reducing development costs.
The ability to predict program performance, whether symbolic or prompt-based, with limited in-domain examples could significantly improve the development and deployment lifecycle of AI systems, moving from trial-and-error to more predictable outcomes.
- · AI developers
- · Enterprises adopting AI
- · AI-powered software providers
- · Projects with unreliable LLM integrations
- · Ad-hoc AI development methodologies
Improved reliability and faster deployment of AI applications across various sectors.
Increased trust in AI systems could accelerate broader adoption and integration into critical infrastructure.
Standardization of AI performance metrics and prediction tools could lead to new regulatory frameworks for AI reliability.
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