A Conditional Mutual Information Analysis of Response Variety in Language Models

Abstract:Large language models provide a variety of responses at inference (generation) that conventionally depend on a temperature parameter. Using a conditional mutual information approach, a quantitative, entropy based analysis of response variety is given. Three limiting behaviors are defined: arbiter, independent (conditionally) and neutral (deterministic) that span no to maximal mutual information. We give examples of each behavior and suggest their application to: temperature analysis, language analysis, prompt compression, and knowledge graphs.

Computation and Language (cs.CL)

arXiv:2611.03836 [cs.CL]

(or arXiv:2611.03836v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2611.03836 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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