A Comparison of Elementary Baselines for BabyLM

Rareș Păpușoi; Sergiu Nisioi

A Comparison of Elementary Baselines for BabyLM

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Abstract

This paper explores multiple simple baselines for the BabyLM challenge, covering random models, elementary predictions based on frequency, n-gram language models, LSTM with several tokenizers (BPE, Unigram, SuperBPE), and GPT-BERT, the winning architecture from the prior BabyLM edition. The evaluation is focused on the BLiMP and BLiMP-Supplement benchmarks. Our experiments show that Strict-Small can sometimes outperform Strict, the fact that performance can be highly sensitive to tokenization and the importance of data efficiency. A simple word-frequency baseline scored unexpectedly high, which led to identifying an evaluation artifact in the pipeline: a system that returns identical logits for both sentences in a minimal pair can achieve maximal accuracy.

Anthology ID:: 2025.babylm-main.16
Volume:: Proceedings of the First BabyLM Workshop
Month:: November
Year:: 2025
Address:: Suzhou, China
Editors:: Lucas Charpentier, Leshem Choshen, Ryan Cotterell, Mustafa Omer Gul, Michael Y. Hu, Jing Liu, Jaap Jumelet, Tal Linzen, Aaron Mueller, Candace Ross, Raj Sanjay Shah, Alex Warstadt, Ethan Gotlieb Wilcox, Adina Williams
Venue:: BabyLM
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 218–225
Language:
URL:: https://aclanthology.org/2025.babylm-main.16/
DOI:
Bibkey:
Cite (ACL):: Rareș Păpușoi and Sergiu Nisioi. 2025. A Comparison of Elementary Baselines for BabyLM. In Proceedings of the First BabyLM Workshop, pages 218–225, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):: A Comparison of Elementary Baselines for BabyLM (Păpușoi & Nisioi, BabyLM 2025)
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PDF:: https://aclanthology.org/2025.babylm-main.16.pdf

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@inproceedings{papusoi-nisioi-2025-comparison,
    title = "A Comparison of Elementary Baselines for {B}aby{LM}",
    author = "P{\u{a}}pușoi, Rareș  and
      Nisioi, Sergiu",
    editor = "Charpentier, Lucas  and
      Choshen, Leshem  and
      Cotterell, Ryan  and
      Gul, Mustafa Omer  and
      Hu, Michael Y.  and
      Liu, Jing  and
      Jumelet, Jaap  and
      Linzen, Tal  and
      Mueller, Aaron  and
      Ross, Candace  and
      Shah, Raj Sanjay  and
      Warstadt, Alex  and
      Wilcox, Ethan Gotlieb  and
      Williams, Adina",
    booktitle = "Proceedings of the First BabyLM Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.babylm-main.16/",
    pages = "218--225",
    ISBN = "TODO",
    abstract = "This paper explores multiple simple baselines for the BabyLM challenge, covering random models, elementary predictions based on frequency, n-gram language models, LSTM with several tokenizers (BPE, Unigram, SuperBPE), and GPT-BERT, the winning architecture from the prior BabyLM edition. The evaluation is focused on the BLiMP and BLiMP-Supplement benchmarks. Our experiments show that Strict-Small can sometimes outperform Strict, the fact that performance can be highly sensitive to tokenization and the importance of data efficiency. A simple word-frequency baseline scored unexpectedly high, which led to identifying an evaluation artifact in the pipeline: a system that returns identical logits for both sentences in a minimal pair can achieve maximal accuracy."
}

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%0 Conference Proceedings
%T A Comparison of Elementary Baselines for BabyLM
%A Păpușoi, Rareș
%A Nisioi, Sergiu
%Y Charpentier, Lucas
%Y Choshen, Leshem
%Y Cotterell, Ryan
%Y Gul, Mustafa Omer
%Y Hu, Michael Y.
%Y Liu, Jing
%Y Jumelet, Jaap
%Y Linzen, Tal
%Y Mueller, Aaron
%Y Ross, Candace
%Y Shah, Raj Sanjay
%Y Warstadt, Alex
%Y Wilcox, Ethan Gotlieb
%Y Williams, Adina
%S Proceedings of the First BabyLM Workshop
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ TODO
%F papusoi-nisioi-2025-comparison
%X This paper explores multiple simple baselines for the BabyLM challenge, covering random models, elementary predictions based on frequency, n-gram language models, LSTM with several tokenizers (BPE, Unigram, SuperBPE), and GPT-BERT, the winning architecture from the prior BabyLM edition. The evaluation is focused on the BLiMP and BLiMP-Supplement benchmarks. Our experiments show that Strict-Small can sometimes outperform Strict, the fact that performance can be highly sensitive to tokenization and the importance of data efficiency. A simple word-frequency baseline scored unexpectedly high, which led to identifying an evaluation artifact in the pipeline: a system that returns identical logits for both sentences in a minimal pair can achieve maximal accuracy.
%U https://aclanthology.org/2025.babylm-main.16/
%P 218-225

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Markdown (Informal)

[A Comparison of Elementary Baselines for BabyLM](https://aclanthology.org/2025.babylm-main.16/) (Păpușoi & Nisioi, BabyLM 2025)

A Comparison of Elementary Baselines for BabyLM (Păpușoi & Nisioi, BabyLM 2025)

ACL

Rareș Păpușoi and Sergiu Nisioi. 2025. A Comparison of Elementary Baselines for BabyLM. In Proceedings of the First BabyLM Workshop, pages 218–225, Suzhou, China. Association for Computational Linguistics.