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Generative language models produce highly abstractive outputs by design, incontrast to extractive responses in search engines. Given this characteristicof LLMs and the resulting implications for content Licensing & Attribution, wepropose the the so-called Extractive-Abstractive axis for benchmarkinggenerative models and highlight the need for developing corresponding metrics,datasets and annotation guidelines. We limit our discussion to the textmodality.

The Extractive-Abstractive Axis: Measuring Content "Borrowing" in Generative Language Models