ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levels
Quick summary
arXiv:2608.03507v1 Announce Type: cross Abstract: Historical language change affects morphology, syntax, semantics, and pragmatics, yet computational studies typically examine these levels with incompatible representations and therefore cannot determine whether they evolve together across languages. We address this problem by asking how the magnitude and direction of change vary across linguistic levels, languages, and historical periods within a single analytical space. We introduce ChronoLens, a framework that combines frozen multilingual language models, feature-aligned crosscoders, and pos
Key takeaways
- arXiv:2608.03507v1 Announce Type: cross Abstract: Historical language change affects morphology, syntax, semantics, and pragmatics, yet computational studies typically examine these levels with incompatible representations and therefore cannot determine whether they evolve together across languages.
- We address this problem by asking how the magnitude and direction of change vary across linguistic levels, languages, and historical periods within a single analytical space.
- We introduce ChronoLens, a framework that combines frozen multilingual language models, feature-aligned crosscoders, and pos
Why it matters
“ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levels” is a product decision that may change how people work with AI. Its value depends on task completion, correction effort and data handling—not simply the presence of a new feature.
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