---
title: "Child-directed speech facilitates production, not comprehension, in BabyLMs — Bunzeck 2026"
description: "They therefore propose a new generative evaluation: the model must complete a lexical frame, i.e., a frequent utterance onset with an… 88 references."
authors: ["Bastian Bunzeck", "Sina Zarrieß"]
year: 2026
venue: "Proceedings of the 30th Conference on Computational Natural Language Learning"
source: "https://aclanthology.org/2026.conll-main.14/"
doi: "10.18653/v1/2026.conll-main.14"
arxiv: "2606.01045"
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/bunzeck-2026-child-directed-speech-facilitates-production/"
---
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# Child-directed speech facilitates production, not comprehension, in BabyLMs

**Authors:** [Bastian Bunzeck](https://md.research.elamur.ai/free/en/authors/bastian-bunzeck/) and [Sina Zarrieß](https://md.research.elamur.ai/free/en/authors/sina-zarriess/) · **Year:** 2026 · **Venue:** Proceedings of the 30th Conference on Computational Natural Language Learning · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.conll-main.14/) · [PDF](https://aclanthology.org/2026.conll-main.14.pdf) · [DOI 10.18653/v1/2026.conll-main.14](https://doi.org/10.18653/v1/2026.conll-main.14) · [arXiv 2606.01045](https://arxiv.org/abs/2606.01045)

## Summary
Previous studies of BabyLM models, that is, language models trained on an amount of data comparable to a child's experience, have mostly found no benefit from child-directed speech (CDS). The authors attribute this to the fact that standard tests, in particular minimal-pair benchmarks, assess only comprehension and do not assess language production. Yet usage-based accounts of how children acquire language place production at their core. They therefore propose a new generative evaluation: the model must complete a lexical frame, i.e., a frequent utterance onset with an unfilled slot. Llama models were trained on CHILDES, the BabyLM corpus, FineWeb-edu, and control datasets. They were compared on comprehension benchmarks and on the new task. The grammaticality of the completions was rated by an LLM-as-a-judge validated against human annotations. Generation dynamics were tracked across training checkpoints, and prediction confidence was measured with entropy. Comprehension and production diverged. On minimal pairs, the models trained on FineWeb-edu win. In contrast, the CDS models produce grammatical completions considerably earlier and concentrate probability on words appropriate for the empty slot. Consequently, if only comprehension is evaluated, the contribution of CDS will be underestimated.

## Key points
- Llama models trained on five datasets were compared: CHILDES, BabyLM, FineWeb-edu, FineWeb-edu-short (with controlled length), and TinyDialogues (with controlled lexical diversity).
- The prompts were six-word frames that were the most frequent in each model's own training data. Examples: "I like to play with my _" from child speech and "It is one of the most _" from FineWeb-edu.
- Already after 10% of training, the CDS models produce meaningful completions such as "toys". The FineWeb-edu models at the same stage generate confused, overly complex constructions.
- The entropy analysis showed that the CDS models predict more confidently: probability falls on specific nouns that semantically fit the argument slot.
- Limitations: the study covers only English and only autoregressive models, and acceptability was rated in binary terms. The authors released the models, prompts, and data in a collection on HuggingFace.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [🧒 Psycholinguistics & language acquisition](https://md.research.elamur.ai/free/en/topics/psycholinguistics/)

## Cite

Cite as: Bastian Bunzeck and Sina Zarrieß. 2026. Child-directed speech facilitates production, not comprehension, in BabyLMs. Proceedings of the 30th Conference on Computational Natural Language Learning.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{bunzeck2026child,
  title = {{Child-directed speech facilitates production, not comprehension, in BabyLMs}},
  author = {Bastian Bunzeck and Sina Zarrieß},
  booktitle = {Proceedings of the 30th Conference on Computational Natural Language Learning},
  year = {2026},
  doi = {10.18653/v1/2026.conll-main.14},
  eprint = {2606.01045},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2026.conll-main.14}
}
```

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