--- title: "Do large language models and humans follow similar learning" description: "The paper asks whether a language model learning a new language picks up grammar in the same… 8 references, cited by 2 papers here." authors: ["Stella Lundqvist", "Murathan Kurfali", "Johan Sjons"] year: 2026 venue: "Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL)" source: "https://aclanthology.org/2026.cdl-1.10/" doi: "10.18653/v1/2026.cdl-1.10" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/lundqvist-2026-do-large-language-models-humans/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lundqvist-2026-do-large-language-models-humans/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/7/) / Do large language models and humans follow similar learning stages? Assessing GPT-2's order of Swedish grammar acquisition within the Processability Theory framework # Do large language models and humans follow similar learning stages? Assessing GPT-2's order of Swedish grammar acquisition within the Processability Theory framework **Authors:** Stella Lundqvist, [Murathan Kurfali](https://md.research.elamur.ai/free/en/authors/murathan-kurfali/) and [Johan Sjons](https://md.research.elamur.ai/free/en/authors/johan-sjons/) · **Year:** 2026 · **Venue:** [Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL)](https://md.research.elamur.ai/free/en/venues/cdl/) ([CDL 2026](https://md.research.elamur.ai/free/en/venues/cdl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.cdl-1.10/) · [PDF](https://aclanthology.org/2026.cdl-1.10.pdf) · [DOI 10.18653/v1/2026.cdl-1.10](https://doi.org/10.18653/v1/2026.cdl-1.10) · [OpenAlex](https://openalex.org/W7166858117) **Citations:** 0 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read ## Summary The paper asks whether a language model learning a new language picks up grammar in the same stepwise order that Processability Theory (PT) predicts for human second-language learners. To simulate an L2 learner, the authors took an English-pretrained GPT-2 and fine-tuned it on Swedish text from OSCAR. The Swedish data was parsed with Stanza and sorted by PT developmental stage. It was then presented in three curricula: random order, increasing complexity and decreasing complexity. They also built SwePT, a new minimal-pair benchmark extracted from the UD Swedish Talbanken and LinES treebanks. SwePT covers nine grammatical phenomena spanning PT stages 2–5. At regular checkpoints the models took acceptability judgment tests (AJT). These were scored with adapted versions of the emergence criterion and implicational scaling. The acquisition order was largely the same across curricula, but it did not follow PT's predicted hierarchy. Performance seemed driven mostly by unigram and n-gram frequency heuristics. The study brings SLA theory into LLM evaluation, though the authors caution that PT and AJT-based testing are not fully compatible. ## Key points - **SwePT benchmark:** nine subsets mapped to PT stages. - Stage 2: SVO, PLU, TNS - Stage 3: ATT - Stage 4: PR_a, PR_b, INV - Stage 5: NEG, INQ - Data and code are released at github.com/stellson/SwePT. - **How the minimal pairs were built:** sentences were extracted by rule-based dependency-tree search over UD CoNLL-U files. Syntactic items were perturbed by reordering constituents and morphological items by reverting forms to the lemma. The PR_b pairs were altered by hand. - **Fine-tuning data:** about 9% of Swedish OSCAR (roughly 680k examples, ~1B tokens) was parsed with Stanza and labeled by structure. A 500k-sentence sample was used so that one epoch fit within 72 hours. - **Main result:** rank correlations show the acquisition order is robust to curriculum ordering, but it violates PT's implicational sequence. - **Limitations and future work:** binary accuracy fits poorly with PT's categorical emergence criterion, and minimal pairs do not control for length or frequency. The authors suggest analyzing model production instead and adding a Swedish model such as GPT-SW3. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Uppsala University](https://md.research.elamur.ai/free/en/universities/uppsala-university/) — University, Uppsala, Sweden · 65 works · 13,164 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,649 works · [💭 Developmental and Educational Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/developmental-and-educational-psychology/) · 523 works 🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works ## Cite Cite as: Stella Lundqvist, Murathan Kurfali and Johan Sjons. 2026. Do large language models and humans follow similar learning stages? Assessing GPT-2's order of Swedish grammar acquisition within the Processability Theory framework. Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{lundqvist2026do, title = {{Do large language models and humans follow similar learning stages? Assessing GPT-2's order of Swedish grammar acquisition within the Processability Theory framework}}, author = {Stella Lundqvist, Murathan Kurfali and Johan Sjons}, booktitle = {Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL)}, year = {2026}, doi = {10.18653/v1/2026.cdl-1.10}, url = {https://doi.org/10.18653/v1/2026.cdl-1.10} } ``` - [← previous Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs](https://md.research.elamur.ai/free/en/papers/zhang-2026-do-images-speak-louder-than/) - [next → Do LLMs Encode Functional Importance of Reasoning Tokens ?](https://md.research.elamur.ai/free/en/papers/singh-2026-do-llms-encode-functional-importance/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/7/) ## References 8 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Gerry TM Altmann and Yuki Kamide. 1999. Incremental interpretation at verbs: Restricting the domain of subsequent reference. Cognition, 73(3):247–264. [↗](https://doi.org/10.1016/s0010-0277(99)00059-1) 2. Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009. [Curriculum learning](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/). In Proceedings of the 26th annual international conference on machine learning, pages 41–48. 3. Terra Blevins, Hila Gonen, and Luke Zettlemoyer. 2022. Analyzing the mono-and cross-lingual pretraining dynamics of multilingual language models. arXiv preprint arXiv:2205.11758. [↗](https://arxiv.org/abs/2205.11758) 4. Aafke Buyl and Alex Housen. 2015. Developmental stages in receptive grammar acquisition: A processability theory account. Second Language Research, 31(4):523–550. [↗](https://doi.org/10.1177/0267658315585905) 5. Cheng-Han Chiang, Sung-Feng Huang, and Hung-yi Lee. 2020. Pretrained language model embryology: The birth of albert. arXiv preprint arXiv:2010.02480. [↗](https://arxiv.org/abs/2010.02480) 6. Leshem Choshen, Guy Hacohen, Daphna Weinshall, and Omri Abend. 2021. The grammar-learning trajectories of neural language models. arXiv preprint arXiv:2109.06096. [↗](https://arxiv.org/abs/2109.06096) 7. Marie-Catherine De Marneffe, Christopher D Manning, Joakim Nivre, and Daniel Zeman. 2021. [Universal dependencies](https://md.research.elamur.ai/free/en/papers/marneffe-2021-universal-dependencies-2/). Computational linguistics, 47(2):255– 308. 8. Bronwen Patricia Dyson and Gisela Håkansson. 2017. Understanding second language processing: A focus on Processability Theory, volume 4. John Benjamins Publishing Company. [find](https://scholar.google.com/scholar?q=Bronwen+Patricia+Dyson+and+Gisela+H%C3%A5kansson%2E+2017%2E+Understanding+second+language+processing%3A+A+focus+on+Processability+Theory%2C+volume+4%2E+John+Benjamins+Publishing+Company%2E) *Updated 1 November 2026*