2026-10-01T21:05:38Z https://dcpapers.dublincore.org/oai

oai:dcpapers.dublincore.org:952623932 2026-09-10 dcmi-2026 openaire

Does AI-Encoded Meaning Align with Human Meaning? Wang, Zhenhua Yao, Aixin Ren, Ming AI is increasingly used to support metadata processing and investigation, which depends on whether AI-encoded meaning aligns with human meaning. However, AI encodes word meaning through distributional and contextual representations, and it remains unclear whether such representations preserve the meaning value of the human system. We answer this question through Zipf’s meaning law, which links word frequency to the number of word meanings. We compare multiple AI-induced meaning estimates with human-measured meaning. To quantify alignment, we propose Meaning-Zipf Deviation (MZD), which covers continuous meaning distributions and measures their divergence with reliability adjustment. Extensive experiments show that human words consistently follow Zipf’s meaning law. AI-encoded meanings also exhibit Zipfian regularities, inheriting part of the statistical structure of human language. However, AI meaning distributions remain flatter than human distributions, with lower scaling exponents and non-negligible MZD values. Larger models do not reduce this gap. AI tends to bind words to context-conditioned senses rather than preserve their broader polysemous potential. Dublin Core Metadata Initiative 2026-09-10 info:eu-repo/semantics/conferenceObject Text application/pdf text/html https://doi.org/10.23106/dcmi.952623932 https://dcpapers.dublincore.org/article/952623932 Dublin Core Metadata Initiative Conference Proceedings eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/