UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Tasks
The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify prompts using natural language, which is inherently ambiguous when multiple objectives must be simultaneously satisfied. In this paper we introduce UtilityMax Prompting, a framework that specifies tasks using formal mathematical language. We reconstruct the task as an influence diagram in which the LLM's answer is the sole decision variable. A utility function is defined over the conditional probability distributions within the diagram, and the LLM is instructed to find the answer that maximises expected utility. This constrains the LLM to reason explicitly about each component of the ob...
| Authors | Ofir Marom |
|---|---|
| Primary category | cs.CL |
| Published | 12 March 2026 |
| Last updated at source | 23 May 2026 |
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