New paper from Prof. Ido Kanter’s research group - How does AI really “understand” text?

A new study recently published on arXiv, and featured on the technology site Quantum Zeitgeist, offers a fascinating look under the hood of advanced language models like BERT — the models behind chatbots, search engines, and machine translation.

Instead of asking only where the model “looks” in a text, the researchers examined how the meaning of words changes as the model processes a sentence.

The result? The model learns on its own to identify sentence boundaries and meaningful segments of text — in a way that closely resembles how humans naturally read and understand language.

Much like heatmaps (such as in the attached image, taken from the Quantum Zeitgeist article) that show where a model focuses to understand an image, this study shows that the same principle applies to text: the model independently learns where the meaning is.

Why does this matter?
Because it brings us closer to understanding how AI actually thinks — and how we can design smarter, more accurate, and more transparent systems.

Authors: Tal Halevi, Yarden Tzach, Ronit D. Gross, Shalom Rosner, Ido Kanter.

Read the paper on arXiv
Feature on Quantum Zeitgeist