arXiv:2607.06145v1 Announce Type: new Abstract: In this paper, we define the quantity of prompting complexity: for a fixed instruction-tuned language model, what is the shortest plausible prompt that makes deterministic decoding produce a target text? It is an LM-relative analogue of resource-bounded Kolmogorov complexity: the prompt is a program, the model interface is the interpreter, and information omitted from the prompt is supplied by the model's weights, training distribution, tokenizer, template, and decoding rule. Unlike classical Kolmogorov complexity, this measure is intentionally n
This paper introduces a novel theoretical framework ('prompting complexity') at a time when 'prompt engineering' is a crucial, yet unquantified, skill in large language model (LLM) interaction and development.
A strategic reader should care because quantifying prompt complexity can lead to more efficient and robust LLM applications, potentially reducing computational costs and increasing predictability of AI outputs.
The ability to formally measure prompt efficiency changes how researchers and developers might approach LLM interaction, moving from heuristics to a more scientific, resource-constrained approach.
- · AI researchers
- · LLM developers
- · Cloud providers (optimization)
- · AI-powered product companies
- · Inefficient prompt engineers
- · LLM training data gatherers (less data might be needed for specific tasks)
The definition of prompting complexity provides a new metric for evaluating the 'intelligence' or 'efficiency' of different instruction-tuned language models.
Improved understanding of prompting complexity could lead to automated prompt generation tools that are far more effective and resource-efficient than current methods.
Formal quantification of prompt effectiveness might influence future LLM architectures, favoring models that achieve desired outputs with minimal prompting resources.
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Read at arXiv cs.CL