Semantic Alignment of AI Models: Concept Collapse, Checkpoint Dynamics, and Cross-Lingual Transfer
2026-08-03 • Computation and Language
Computation and LanguageMachine Learning
AI summaryⓘ
The authors explain that judging language models just by their answers misses how well the models really understand language concepts. They show that comparing the models' internal data patterns with clear, human-made knowledge maps can give better insight. Using math called topology, they compare these complex data shapes to simpler ones like ontologies, making it easier to understand what the model knows. Their method also works in different languages and can track how models change over time.
language model benchmarkingsemantic structureembedding spacestopological methodsontologiesknowledge graphsmulti-modal alignmentphrase understandingcross-lingual testing
Authors
Tyler Ashoff, Jordan Rodu
Abstract
Language model benchmarking is a difficult task. Outcome reasoning alone does not test the model's conceptualization of language and popular open-source benchmarks are quickly saturated or ingested as training data. It is important to test the model's output, but augmenting these tests by characterizing semantic structure gives more insight to how models relate abstract concepts. However, the high dimensional embedding spaces are not easy to interpret. This work demonstrates how topological methods can be used to rigorously compare these spaces to low dimensional and interpretable baselines like ontologies and curated knowledge graphs. These multi-modal alignment tests make it possible to track model adaptations and test phrase understanding across multiple languages.