Ma & Xu (2025)

Implicit In-Context Learning: Evidence from Artificial Language Experiments

arXiv

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In Summary

Humans acquire language implicitly, absorbing complex patterns without explicit awareness, but whether LLMs show human-like pattern extraction during in-context learning at inference remains unclear. The authors adapt three classic artificial language learning experiments spanning morphology, morphosyntax, and syntax to evaluate implicit learning in gpt-4o and o3-mini. Results show alignment between models and human behaviour that is specific to linguistic domain: o3-mini aligns better in morphology, and both models align in syntax.

Reference

Ma, X., & Xu, Q. (2025). Implicit in-context learning: Evidence from artificial language experiments. arXiv. https://arxiv.org/abs/2503.24190

What Rests on This

1 mechanism cites this source. Each finding below is quoted as it appears on that mechanism's own page.

Procedural Memory Memory Systems

Measured in AI

A 2025 preprint ran gpt-4o and o3-mini on three classic artificial-language experiments with published human data: on a finite-state grammar task (15 people per grammar) both models improved across blocks and found the simpler grammar easier, as people did, but on a morphosyntax task neither model improved over trials, and on a morphology task only o3-mini matched human regularization
Measured in AI

That comparison is approximate and does not set the rating: models got shorter versions of the tasks (e.g., 120 rather than 480 sentences), could still see the training items in the conversation while people had to remember them, and results were compared as learning patterns rather than on a common accuracy scale