aiqbench analysis
This study demonstrates that neural language models trained on next-token prediction can spontaneously recover both coarse word classes and finer syntactic constructions through successor representations, revealing a rich predictive geometry without explicit supervision. The findings suggest that linguistic structure emerges naturally from future-word distributions across multiple temporal horizons, offering new insights into how deep learning encodes complex grammatical patterns.
Word-Class and Construction-Like Structure Emerges in Neural Successor Representations Trained on Natural Language
arXiv:2605.24585v2 Announce Type: replace Abstract: Neural language models are typically trained on next-token prediction, although linguistic structure spans multiple temporal scales. Successor representations (SRs) make this horizon explicit by encoding discounted distributions over future states. Here, we ask whether such predictive representations can recover not only word classes, but also finer functional and construction-like structure from natural language. A residual network trained on WikiText-103 predicts SR distributions at three horizons without part-of-speech supervision. At the shortest horizon, unsupervised clustering robustly recovers nouns, verbs, and adjectives, while directed inter-cluster transitions reproduce familiar syntactic asymmetries. At finer resolutions and across 13 part-of-speech categories, the same geometry reveals semantic-functional groupings that cross category boundaries and directed relations tracing candidate date, measurement, and title-name constructions. Part-of-speech agreement declines as the predictive horizon lengthens. These results suggest that word classes are coarse regions within a richer predictive geometry in which categorical and construction-like linguistic structure emerge from future-word distributions.
Abstract:Neural language models are typically trained on next-token prediction, although linguistic structure spans multiple temporal scales. Successor representations (SRs) make this horizon explicit by encoding discounted distributions over future states. Here, we ask whether such predictive representations can recover not only word classes, but also finer functional and construction-like structure from natural language. A residual network trained on WikiText-103 predicts SR distributions at three horizons without part-of-speech supervision. At the shortest horizon, unsupervised clustering robustly recovers nouns, verbs, and adjectives, while directed inter-cluster transitions reproduce familiar syntactic asymmetries. At finer resolutions and across 13 part-of-speech categories, the same geometry reveals semantic-functional groupings that cross category boundaries and directed relations tracing candidate date, measurement, and title-name constructions. Part-of-speech agreement declines as the predictive horizon lengthens. These results suggest that word classes are coarse regions within a richer predictive geometry in which categorical and construction-like linguistic structure emerge from future-word distributions.
Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC)
arXiv:2605.24585 [cs.CL]
(or arXiv:2605.24585v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.24585 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 246 words
