arXiv:2603.11583v4 Announce Type: replace Abstract: 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 proliferation of LLMs in complex tasks necessitates more robust and less ambiguous prompting methods, moving beyond natural language limitations.
This framework offers a path to more reliable and controllable LLM behavior in critical applications, addressing current inconsistencies and 'hallucinations'.
LLM task specification can shift from ambiguous natural language to formal, mathematical definitions, enabling more precise multi-objective optimization.
- · AI developers
- · Enterprises deploying LLMs for complex workflows
- · SaaS companies integrating advanced LLM capabilities
- · Providers of purely natural language prompt engineering services
Increased determinism and reduced ambiguity in high-stakes LLM applications across various industries.
Accelerated adoption of LLMs in regulated or sensitive domains due to enhanced reliability and verifiability.
The development of specialized formal languages and tools for prompt design, fostering a new sub-discipline within AI.
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Read at arXiv cs.CL