The Information Efficiency Metric (IEM): An Info-Metric Approach for Quantifying AI Language Model Performance
Year: 2026
Type: article
Abstract: The interaction between humans and artificial intelligence has become a critical channel for information exchange, yet no quantitative, theoretically grounded framework exists for measuring information efficiency in human–AI communication. This study empirically validated an info-metrics framework operationalizing information efficiency through three dimensions: information density (D), relevance (R) and redundancy (Q) synthesized into an information efficiency metric (IEM). We analyzed 60 AI responses from ChatGPT 5.2 and Claude Opus 4.5 across factual, analytical and creative question types using combined coding, automated structural measures and human evaluation of informational units. Results showed that information density and relevance positively contributed to IEM, while redundancy had a negative contribution. Efficiency varied by task type, with factual prompts showing highest variability across models and highest efficiency. Contrary to expectations, creative responses did not exhibit higher redundancy suggesting that expressive diversity does not necessarily constitute informational noise. The framework offers a task-sensitive, theoretically grounded approach to evaluate human–AI information exchange beyond correctness or subjective quality judgement, supporting systems-oriented optimization of conversational AI protocols.
Source: Information
DOI: 10.3390/info17030307
Authors: Ljerka Luić, Maja Barbić, Marijana Rončević
Institution: University North
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7140087103