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REVIEW 4 major objections 6 minor 39 references

BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Training on variation sets helps grammar and GLUE scores, not world knowledge.

desk verdict The abstract claims a VS benefit that the tables do not secure; the real value is in the controlled design and the honest null. read the letter →

arxiv 2411.09587 v2 pith:ZAKOCFIE submitted 2024-11-14 cs.CL

Akari Haga , Akiyo Fukatsu , Miyu Oba , Arianna Bisazza , Yohei Oseki This is my paper · ORCID
classification cs.CL
keywords variationsetschild-directedspeechlanguagemodeltrainingefficiencydataaugmentationGPT-2BLiMPGLUEEWOK
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks whether variation sets—clusters of consecutive utterances that repeat the same intent with slightly different wording, a hallmark of child-directed speech—make small language models learn more efficiently. To isolate this property, the authors generate artificial variation sets, mix them into a corpus of transcribed caregiver-child speech at rates from 0% to 100%, and train GPT-2 from scratch on each mixture. The central result is that the benefit depends on the benchmark: BLiMP and GLUE scores improve with the presence of variation sets, while EWOK world-knowledge scores do not. A second, counterintuitive finding is that presenting the variation sets in shuffled order is often better than presenting them consecutively as they occur in natural speech.

What carries the argument

The central object is the variation set (VS): a block of successive utterances that keep a consistent semantic intent while substituting words and adding, deleting, or reordering phrases. Because natural VSs are hard to control, the authors construct artificial VSs with a general-purpose language model using a prompt based on the standard definition of variation sets, mix them into shuffled child-directed speech at six ratios (0, 20, 40, 60, 80, 100%), and feed them to GPT-2 using two input schemes: concatenating each set into one sequence, or placing each sentence in adjacent batches so the model updates parameters between sentences. The comparison between these consecutive and shuffled conditions is what lets the paper separate the presence of variation from its order.

What would settle it

Re-run the same training and evaluation protocol using naturally occurring variation sets extracted from a caregiver-child corpus instead of the artificial paraphrases, matched for proportion; if BLiMP and GLUE no longer benefit from their presence, the reported effect is a property of the synthetic data rather than of the child-directed pattern they imitate.

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Extended reading notes

Core claim

The paper's central claim is that variation sets can help a small autoregressive model learn from a child-directed dataset, but only under specific conditions. Using artificial variation sets written by a large language model and mixing them into 10 million words of transcribed caregiver-child speech, the authors find that the optimal fraction of variation sets is not a single number: grammatical knowledge measured by BLiMP peaks at lower proportions in one presentation mode, GLUE fine-tuning improves as the proportion rises, and EWOK never benefits. Comparing consecutive presentation with shuffled presentation shows that the shuffled order often performs as well or better, which the authors read as evidence that the useful signal may be the presence of rephrasings rather than their adjacency. The results therefore qualify, rather than confirm, the hypothesis that child-directed variation sets improve training data efficiency.

Load-bearing premise

The experiments assume that the paraphrases generated by a large language model capture the same helpful property as natural variation sets in child-directed speech; if they do not, the measured effects describe a synthetic construction rather than the real speech pattern.

Editorial extensions

If this is right

  • If the central result holds, adding variation sets to child-directed training data can improve scores on grammatical (BLiMP) and fine-tuned language-understanding (GLUE) benchmarks without improving, and sometimes slightly lowering, world-knowledge scores (EWOK).
  • The optimal proportion of variation sets is task-dependent, so a single best augmentation rate cannot be recommended across benchmarks.
  • Shuffled presentation of variation sets often matches or beats consecutive presentation, implying that the model's gain does not require the exact sequential ordering found in child-directed speech.
  • The effect persists in one-epoch training, though it is not stronger than in three-epoch training, so the benefit is not simply a multi-epoch repetition effect.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An extension the paper does not run: compare a fixed amount of paraphrastic variation against the same number of verbatim repetitions, to separate the effect of variation from the effect of reduced lexical diversity.
  • If the artificial paraphrases are cleaner and more uniform than natural child-directed variation sets, real-world CDS may produce weaker gains; testing with naturally extracted variation sets would resolve this.
  • The shuffled-order advantage hints that the model learns from encountering paraphrases as separate events, a mechanism the paper does not claim.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper investigates whether variation sets (VSs), consecutive utterances with similar intent and slight lexical/syntactic variation that are common in child-directed speech (CDS), can improve the data efficiency of small language models. The authors generate synthetic VSs with gpt4o-mini from CDS utterances, mix them into otherwise shuffled CDS data at ratios of 0%, 20%, 40%, 60%, 80%, and 100%, and train a 124M-parameter GPT-2 from scratch using two presentation methods: sequential concatenation of each VS and placement of VS sentences into adjacent batches. Models are evaluated on BLiMP, BLiMP Supplement, EWOK, and GLUE after 1 and 3 training epochs. The central claim is that BLiMP and GLUE scores benefit from the presence of VSs while EWOK scores do not, and that shuffled presentation is often competitive with or better than the consecutive presentation found in CDS.

Significance. The question is timely for the BabyLM line of work and for the broader goal of identifying which properties of child-directed speech actually help language models. The controlled manipulation of VS proportion, the inclusion of shuffled baselines, and the use of multiple held-out benchmarks are appropriate strengths, and the paper is transparent about its limitations in Section 7. If the reported effects were statistically robust, the paper would provide a useful data point on how input repetition and variation shape small-model pretraining. At present, however, the contribution is closer to an exploratory study: the central claim rests on sub-point differences without variance estimates or significance testing, and several configurations in the authors' own tables point in the opposite direction.

major comments (4)
  1. [Tables 2-5] The main claim is not statistically secured. Tables 2-5 report only point estimates labeled "Averaged Scores" and no standard deviation, confidence interval, or significance test, and the number of random seeds is not stated. The abstract's central claim relies on differences such as Table 3 BLiMP 60.8 at 0% VS versus 61.6 at 100% VS and GLUE 68.1 versus 68.9, which are below one percentage point and comparable to or smaller than differences between the consecutive and shuffled conditions elsewhere in the same tables (e.g., Table 2 BLiMP at 60%: 57.9 consecutive vs. 60.9 shuffled). Given that a 124M-parameter model trained from scratch is stochastic and that dozens of configurations are compared, these differences are currently indistinguishable from seed noise. The authors should report variance over multiple seeds and a significance test or bootstrap confidence interval, and they should temper the causal wording of the abstract accordingly.
  2. [Section 5.3 and Tables 4-5] The one-epoch results and the shuffled controls undermine the general claim in the abstract. In Section 5.3 the authors state that under the Adjacent Batch Method "none of the metrics showed a significant advantage for the consecutive condition over the shuffled condition," and in the one-epoch Sequential Concatenation condition BLiMP decreases monotonically with VS ratio (58.3 at 0% to 56.6 at 100%) while the shuffled condition is always higher. Section 5.1 further reports that the shuffled condition improves the three-epoch macro average by 0.89% over the consecutive condition. Thus the assertion that "BLiMP and GLUE scores benefit from the presence of VSs" holds, if at all, only for selected configurations and is often reversed by the shuffle control; this qualification should be stated prominently rather than relegated to the discussion.
  3. [Sections 4.2 and 7] The synthetic VSs are a proxy whose fidelity to natural CDS variation sets is unvalidated, and the paper's own limitations section concedes that "gpt4o-mini does not necessarily generate VSs that closely resemble natural VSs." The generation prompt in Appendix A specifies only generic rephrasing operations and a child-friendly vocabulary constraint, and the CDS corpus is shuffled specifically to remove natural VSs. Consequently, the experiment measures the effect of gpt4o-mini rephrasings on GPT-2 training, not necessarily the effect of the CDS property named in the title. The authors should either add a validation step (e.g., human judgments or automatic comparison with naturally occurring VSs) or explicitly reframe the claim as being about synthetic CDS-inspired variations.
  4. [Section 5.4] The VS ratio is confounded with lexical diversity, and this confound is acknowledged but not controlled. Increasing the VS ratio replaces diverse CDS utterances with repeated rephrasings of the same intent, so any observed gain could be due to reduced lexical diversity or less noisy full-sentence input rather than to the structural relationship among utterances within a VS. The authors note in Section 5.4 that they are planning a control condition with identical repetitions; until such a control is run, the causal attribution of the results to variation sets specifically is not supported. At minimum, the paper should present the current findings as exploratory and correlational with respect to VS structure.
minor comments (6)
  1. [Tables 2-5] The table captions say "Averaged Scores" but do not specify what the scores are averaged over; clarify whether this is across seeds, across evaluation examples, or across tasks, and state whether the 0% rows in Tables 2/3 and 4/5 correspond to the same underlying runs.
  2. [Appendix B] The hyperparameter table should report the random seed(s) used and any convergence or early-stopping criterion, since the text in Section 5 refers to "after the model training has converged, specifically the results after 3 epochs."
  3. [Section 4.3] The shuffling procedure should be defined more precisely, namely whether shuffling is applied at the sentence level, utterance level, or document level, because the interpretation of the shuffle condition as a control for consecutive VS presentation depends on this detail.
  4. [Ethics Statement] There is a typo: "prepossessing" should be "preprocessing."
  5. [Section 5.4] The sentence "We are currently planning an experiment where a given amount of variations will be compared to a similar amount of identical repetitions" is future work and should be moved to a future-work paragraph rather than appearing in the results and discussion section.
  6. [Section 5.3] The motivation for the one-epoch condition says children see each instance only once, but the models in the main condition are trained for multiple epochs on the same data; the authors should clarify how the one-epoch condition relates to the CDS acquisition argument.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the VS-ratio manipulation is an experimental independent variable and all evaluations use external, held-out benchmarks.

full rationale

The paper's central claim compares scores on BLiMP, EWOK, and GLUE after training GPT-2 on corpora containing different proportions of artificially generated variation sets (VSs). The VS proportion is an experimental manipulation, not a fitted parameter: it is varied across 0%, 20%, 40%, 60%, 80%, and 100% independently of the evaluation results, and no training hyperparameter is chosen to optimize any benchmark. The shuffled condition is a genuine control for the order of utterance presentation, so the consecutive-vs-shuffled comparison is not definitional. The evaluation benchmarks are external, pre-existing, and not used to construct the training data, preventing a self-definitional loop. The cited pilot work by Katano (2024) is described as yielding no significant effect and is not used as load-bearing evidence for the present conclusions; the paper explicitly introduces synthetic VSs to overcome that pilot's controllability issue. Section 7's admitted limitations, such as gpt4o-mini possibly generating VSs unlike natural ones and the shuffling procedure disrupting natural VSs, concern construct validity and generalizability, not circularity. These limitations weaken the causal interpretation of the results but do not make any prediction equivalent to the input by construction. No fitted quantity is renamed as a prediction, and no load-bearing argument reduces to a self-citation. The derivation chain is therefore self-contained with respect to the claims it makes.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new theoretical entities. Its load-bearing assumptions are domain-level: the validity of the benchmarks as acquisition proxies, the fidelity of gpt4o-mini-generated VSs to natural VSs, the effectiveness of shuffling as a control, and the representativeness of GPT-2. These are empirical choices rather than fitted parameters, and the paper itself acknowledges the first and second in its Limitations section.

free parameters (2)
  • minimum utterance length = 3 words
    Utterances of fewer than three words were removed before VS generation (Section 4.2). This hand-chosen threshold affects which sentences enter the training data and could interact with VS effects.
  • training epochs = 3 and 1
    Results are reported after 3 epochs, described as converged, and after 1 epoch to test whether VS effects diminish with repetition (Sections 5.1, 5.3). The epoch choice is a design decision, not a fitted parameter, but it directly shapes the reported findings.
assumptions (4)
  • domain assumption BLiMP, EWOK, and GLUE scores are valid proxies for the linguistic competence that variation sets might improve.
    The paper treats these benchmarks as measuring grammar, world knowledge, and general language understanding (Section 4.4). Their construct validity for the acquisition question is assumed rather than tested.
  • domain assumption Artificial VSs generated by gpt4o-mini following the Kuntay and Slobin (1996) definition are a valid proxy for natural VSs.
    The entire manipulation rests on this proxy. The paper flags the risk in Section 7: gpt4o-mini may not generate VSs that closely resemble natural VSs, and no validation against natural VSs is provided.
  • domain assumption Shuffling CDS sentences removes natural VSs and isolates the effect of VS order and proportion.
    Section 4.3 states that all CDS except artificial VSs was shuffled to ensure no natural VSs are included, but shuffling also changes many other distributional properties, such as topic coherence and local lexical repetition, which could confound the consecutive-vs-shuffled comparisons.
  • domain assumption GPT-2 trained from scratch with the given hyperparameters is a representative model for studying input structure effects in sample-efficient learning.
    The choice of GPT-2 and the hyperparameters in Appendix B are assumed to be a reasonable stand-in for small auto-regressive LMs. Other architectures or scales might respond differently to VS augmentation.

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Cite this review

Pith. "Pith review of BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency." pith.science (2026). https://pith.science/paper/ZAKOCFIE

@misc{pith2026241109587,
  author       = {Pith},
  title        = {Pith review of: BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZAKOCFIE}},
  note         = {Machine review of arXiv:2411.09587}
}
read the original abstract

While current large language models have achieved a remarkable success, their data efficiency remains a challenge to overcome. Recently it has been suggested that child-directed speech (CDS) can improve training data efficiency of modern language models based on Transformer neural networks. However, it is not yet understood which specific properties of CDS are effective for training these models. In the context of the BabyLM Challenge, we focus on Variation Sets (VSs), sets of consecutive utterances expressing a similar intent with slightly different words and structures, which are ubiquitous in CDS. To assess the impact of VSs on training data efficiency, we augment CDS data with different proportions of artificial VSs and use these datasets to train an auto-regressive model, GPT-2. We find that the best proportion of VSs depends on the evaluation benchmark: BLiMP and GLUE scores benefit from the presence of VSs, but EWOK scores do not. Additionally, the results vary depending on multiple factors such as the number of epochs and the order of utterance presentation. Taken together, these findings suggest that VSs can have a beneficial influence on language models, while leaving room for further investigation.

Figures

Figures reproduced from arXiv: 2411.09587 by the authors.

Figure 1
Figure 1. Two methods for inputting VSs to the model during training. Each figure illustrates an example with a [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

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Reference graph

Works this paper leans on

39 extracted references · 25 canonical work pages

  1. [1]

    Arielle Borovsky, Jeffrey L Elman, and Anne Fernald. 2012. Knowing a lot for one's age: Vocabulary skill and not age is associated with anticipatory incremental sentence interpretation in children and adults. J Exp Child Psychol, 112(4):417--436

    work page 2012

  2. [2]

    Peter Brodsky and Heidi Waterfall. 2007. https://escholarship.org/uc/item/54k371nk Characterizing motherese: On the computational structure of child-directed language . In Proceedings of the Annual Meeting of the Cognitive Science Society, Nashville, Tennessee. Cognitive Sciency Society

    work page 2007

  3. [3]

    Thea Cameron-Faulkner, Elena Lieven, and Michael Tomasello. 2003. https://doi.org/10.1207/s15516709cog2706\_2 A construction based analysis of child directed speech . Cognitive Science, 27(6):843--873

    work page doi:10.1207/s15516709cog2706 2003

  4. [4]

    Hu, Tal Linzen, Aaron Mueller, Candace Ross, Alex Warstadt, Ethan Wilcox, Adina Williams, and Chengxu Zhuang

    Leshem Choshen, Ryan Cotterell, Michael Y. Hu, Tal Linzen, Aaron Mueller, Candace Ross, Alex Warstadt, Ethan Wilcox, Adina Williams, and Chengxu Zhuang. 2024. https://arxiv.org/abs/2404.06214 [call for papers] the 2nd BabyLM C hallenge: Sample-efficient pretraining on a developmentally plausible corpus . Computing Research Repository, arXiv:2404.06214

    arXiv 2024

  5. [5]

    Alejandrina Cristia, Emmanuel Dupoux, Michael Gurven, and Jonathan Stieglitz. 2019. Child-directed speech is infrequent in a forager-farmer population: A time allocation study. Child development, 90(3):759--773

    work page 2019

  6. [6]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long a...

    doi:10.18653/v1/n19-1423 2019

  7. [7]

    Carol B. Farwell. 1975. https://doi.org/10.1159/000271489 The Language Spoken to Children . Human Development, 18(4):288--309

    work page doi:10.1159/000271489 1975

  8. [8]

    Anne Fernald. 1985. https://doi.org/10.1016/S0163-6383(85)80005-9 Four-month-old infants prefer to listen to motherese . Infant Behavior and Development, 8(2):181--195

    work page doi:10.1016/s0163-6383(85)80005-9 1985

Show all 39 references
  1. [9]

    Anne Fernald, Traute Taeschner, Judy Dunn, Mechthild Papousek, B\'en\'edicte de Boysson-Bardies , and Ikuko Fukui. 1989. A cross-language study of prosodic modifications in mothers' and fathers' speech to preverbal infants. Journal of Child Language, 16(3):477--501

    1989

  2. [10]

    Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac'h, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang S...

    2023 doi

  3. [11]

    Erika Hoff-Ginsberg. 1986. Function and structure in maternal speech: Their relation to the child’s development of syntax. Developmental Pyschology, 22(2):155--163

    1986

  4. [12]

    Huebner, Elior Sulem, Fisher Cynthia, and Dan Roth

    Philip A. Huebner, Elior Sulem, Fisher Cynthia, and Dan Roth. 2021. https://doi.org/10.18653/v1/2021.conll-1.49 B aby BERT a: Learning more grammar with small-scale child-directed language . In Proceedings of the 25th Conference on Computational Natural Language Learning, page...

    2021 doi

  5. [13]

    Ivanova, Aalok Sathe, Benjamin Lipkin, Unnathi Kumar, Setayesh Radkani, Thomas H

    Anna A. Ivanova, Aalok Sathe, Benjamin Lipkin, Unnathi Kumar, Setayesh Radkani, Thomas H. Clark, Carina Kauf, Jennifer Hu, R. T. Pramod, Gabriel Grand, Vivian Paulun, Maria Ryskina, Ekin Akyürek, Ethan Wilcox, Nafisa Rashid, Leshem Choshen, Roger Levy, Evelina Fedorenko, Joshu...

    2024 arXiv

  6. [14]

    Peter W. Jusczyk. 1997. The discovery of spoken language. MIT Press

    1997

  7. [15]

    Harufumi Katano. 2024. Syntax acquisition in language models with small datasets: D ata efficiency using variation sets [in J apanese]. Bachelor's thesis

    2024

  8. [16]

    Nenagh Kemp, Elena Lieven, and Michael Tomasello. 2005. Young children's knowledge of the ``determiner'' and ``adjective'' categories. J Speech Lang Hear Res, 48(3):592--609

    2005

  9. [17]

    Vera Kempe, Mitsuhiko Ota, and Sonja Schaeffler. 2024. https://doi.org/10.1016/j.dr.2024.101121 Does child-directed speech facilitate language development in all domains? a study space analysis of the existing evidence . Developmental Review, 72:101121

    2024

  10. [18]

    Katrin Kirchhoff and Steven Schimmel. 2005. https://doi.org/10.1121/1.1869172 Statistical properties of infant-directed versus adult-directed speech: Insights from speech recognition . The Journal of the Acoustical Society of America, 117(4):2238--2246

    2005 doi

  11. [19]

    Küntay and Dan I

    Aylin C. Küntay and Dan I. Slobin. 1996. Listening to a Turkish mother: Some puzzles for acquisition, chapter 17. Lawrence Erlbaum

    1996

  12. [20]

    Lester, Steven Moran, Aylin C

    Nicholas A. Lester, Steven Moran, Aylin C. Küntay, Shanley E.M. Allen, Barbara Pfeiler, and Sabine Stoll. 2022. https://doi.org/10.1016/j.cognition.2021.104986 Detecting structured repetition in child-surrounding speech: Evidence from maximally diverse languages . Cognition, 2...

    2022

  13. [21]

    Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. https://api.semanticscholar.org/CorpusID:198953378 Roberta: A robustly optimized bert pretraining approach . ArXiv, abs/1907.11692

    2019 arXiv

  14. [22]

    Brian MacWhinney. 2000. The CHILDES Project: Tools for analyzing talk. Third Edition . Lawrence Erlbaum Associates

    2000

  15. [23]

    Aaron Mueller and Tal Linzen. 2023. https://doi.org/10.18653/v1/2023.acl-long.629 How to plant trees in language models: Data and architectural effects on the emergence of syntactic inductive biases . In Proceedings of the 61st Annual Meeting of the Association for Computation...

    2023 doi

  16. [24]

    Elissa L. Newport. 1990. https://doi.org/10.1016/0364-0213(90)90024-Q Maturational constraints on language learning . Cognitive Science, 14(1):11--28

    1990 doi

  17. [25]

    Waterfall, and Shimon Edelman

    Luca Onnis, Heidi R. Waterfall, and Shimon Edelman. 2008. https://doi.org/10.1016/j.cognition.2008.10.004 Learn locally, act globally: Learning language from variation set cues . Cognition, 109(3):423--430

    2008 doi

  18. [26]

    Rhea Paul. 1981. Analyzing Complex Sentence Development, pages 36--71. University Park Press

    1981

  19. [27]

    Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf Improving language understanding by generative pre-training

    2018

  20. [28]

    Tracy Reuter, Arielle Borovsky, and Casey Lew-Williams. 2019. Predict and redirect: Prediction errors support children's word learning. Dev Psychol, 55(8):1656--1665

    2019

  21. [29]

    Meredith L. Rowe. 2012. https://doi.org/10.1111/j.1467-8624.2012.01805.x A longitudinal investigation of the role of quantity and quality of child-directed speech in vocabulary development . Child Development, 83(5):1762--1774

    2012

  22. [30]

    Catherine E. Snow. 1972. http://www.jstor.org/stable/1127555 Mothers' speech to children learning language . Child Development, 43(2):549--565

    1972

  23. [31]

    Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018. https://doi.org/10.18653/v1/W18-5446 GLUE : A multi-task benchmark and analysis platform for natural language understanding . In Proceedings of the 2018 EMNLP Workshop B lackbox NLP : A...

    2018 doi

  24. [32]

    Alex Warstadt and Samuel R. Bowman. 2022. https://arxiv.org/abs/2208.07998 What artificial neural networks can tell us about human language acquisition . Preprint, arXiv:2208.07998

    2022 arXiv

  25. [33]

    Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R. Bowman. 2020. https://doi.org/10.1162/tacl_a_00321 BL i MP : The benchmark of linguistic minimal pairs for E nglish . Transactions of the Association for Computational Linguistic...

    2020 doi

  26. [34]

    Waterfall

    Heidi R. Waterfall. 2006. A little change is a good thing: Feature theory, language acquisition and variation sets. Ph.D. thesis, University of Chicago

    2006

  27. [35]

    Ann Weber, Anne Fernald, and Yatma Diop. 2017. When cultural norms discourage talking to babies: Effectiveness of a parenting program in rural senegal. Child Development, 88(5):1513--1526

    2017

  28. [36]

    Mats Wir \'e n, Kristina Nilsson Bj \"o rkenstam, Gintar \.e Grigonyt \.e , and Elisabet Eir Cortes. 2016. https://doi.org/10.18653/v1/W16-1907 Longitudinal studies of variation sets in child-directed speech . In Proceedings of the 7th Workshop on Cognitive Aspects of Computat...

    2016 doi

  29. [37]

    Daum, and Sabine Stoll

    Guanghao You, Balthasar Bickel, Moritz M. Daum, and Sabine Stoll. 2021. https://doi.org/10.1038/s41598-021-95392-x Child-directed speech is optimized for syntax-free semantic inference . Scientific Reports, 11(1):16527

    2021 doi

  30. [38]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  31. [39]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

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Reviewed August 12, 2026 · model on record in the stance chip above.