Implicit In-Context Learning: Evidence from Artificial Language Experiments
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Abstract (arXiv)
Humans acquire language through implicit learning, absorbing complex patterns without explicit awareness. While LLMs demonstrate impressive linguistic capabilities, it remains unclear whether they exhibit human-like pattern recognition during in-context learning at inferencing level. We adapted three classic artificial language learning experiments spanning morphology, morphosyntax, and syntax to systematically evaluate implicit learning at inferencing level in two state-of-the-art OpenAI models: gpt-4o and o3-mini. Our results reveal linguistic domain-specific alignment between models and human behaviors, o3-mini aligns better in morphology while both models align in syntax.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 31 Mar 2025.
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11 of 33 references.
References · 11
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Using cognitive psychology to understand GPT-3cited by 398
- 2022Data Distributional Properties Drive Emergent In-Context Learning in Transformerscited by 309
- 2022Emergent Analogical Reasoning in Large Language Modelscited by 240
- 2022Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizerscited by 204
- 2024Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worsecited by 109
- 2023Large Language Models as Analogical Reasonerscited by 107
- 2023Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selectioncited by 37
- 2024Revisiting In-context Learning Inference Circuit in Large Language Modelscited by 17
- 2024A Survey to Recent Progress Towards Understanding In-Context Learningcited by 14
- 2025Levels of Analysis for Large Language Modelscited by 10
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Citing papers per year: 2026: 2.
2 papers.