Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models

• URL: http://arxiv.org/abs/2505.23689v1
• Date: Thu, 29 May 2025 17:25:36 GMT
• Title: Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models
• Authors: Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, Arianna Bisazza,
• Abstract summary: We show that language models trained on English Child-Directed Language (CDL) reach similar syntactic abilities as LMs trained on larger amounts of adult-directed written text.<n>We test this by comparing models trained on CDL vs. Wikipedia across two LM objectives (masked and causal), three languages (English, French, German), and three syntactic minimal-pair benchmarks.<n>Our results on these benchmarks show inconsistent benefits of CDL, which in most cases is outperformed by Wikipedia models.
• Score: 5.636296752147828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can reach similar syntactic abilities as LMs trained on much larger amounts of adult-directed written text, suggesting that CDL could provide more effective LM training material than the commonly used internet-crawled data. However, the generalizability of these results across languages, model types, and evaluation settings remains unclear. We test this by comparing models trained on CDL vs. Wikipedia across two LM objectives (masked and causal), three languages (English, French, German), and three syntactic minimal-pair benchmarks. Our results on these benchmarks show inconsistent benefits of CDL, which in most cases is outperformed by Wikipedia models. We then identify various shortcomings in previous benchmarks, and introduce a novel testing methodology, FIT-CLAMS, which uses a frequency-controlled design to enable balanced comparisons across training corpora. Through minimal pair evaluations and regression analysis we show that training on CDL does not yield stronger generalizations for acquiring syntax and highlight the importance of controlling for frequency effects when evaluating syntactic ability.

Related papers

• Optimizing Language Models for Crosslingual Knowledge Consistency [90.86445137816942]
  Large language models are known to often exhibit inconsistent knowledge.<n>This is particularly problematic in multilingual scenarios, where models are likely to be asked similar questions in different languages.<n>In this work, we show that this issue can be mitigated using reinforcement learning with a structured reward function.
  arXiv  Detail & Related papers  (2026-03-04T23:36:55Z)
• Beyond a Single Reference: Training and Evaluation with Paraphrases in Sign Language Translation [1.9102169745315323]
  Most Sign Language Translation (SLT) corpora pair each signed utterance with a single written-language reference.<n>This limitation constrains both model training and evaluation.<n>We introduce BLEUpara, an extension of BLEU that evaluates translations against multiple paraphrased references.
  arXiv  Detail & Related papers  (2026-01-29T00:02:19Z)
• Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings [1.1556013985948772]
  We evaluate transferability of pre-trained language models to low-resource Indonesian local languages.<n>We group the target languages into three categories: seen, partially seen, and unseen.<n> Multilingual models perform best on seen languages, moderately on partially seen ones, and poorly on unseen languages.<n>We find that MAD-X significantly improves performance, especially for seen and partially seen languages, without requiring labeled data in the target language.
  arXiv  Detail & Related papers  (2025-07-02T12:17:55Z)
• The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs [54.59207567677249]
  Large language models (LLMs) still struggle across tasks outside of high-resource languages.<n>In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce.
  arXiv  Detail & Related papers  (2025-05-23T20:28:31Z)
• Randomly Sampled Language Reasoning Problems Explain Limits of LLMs [8.146860674148044]
  LLMs have revolutionized the field of machine learning due to their high performance across a range of tasks.<n>They are known to perform poorly in planning, hallucinate false answers, have degraded performance on less canonical versions of the same task, and answer incorrectly on a variety of specific prompts.<n>This paper attempts to isolate novelty as a factor in LLM underperformance.
  arXiv  Detail & Related papers  (2025-01-06T07:57:51Z)
• One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks [68.33068005789116]
  We present the first study aimed at objectively assessing the fairness and robustness of Large Language Models (LLMs) in handling dialects in canonical reasoning tasks.<n>We hire AAVE speakers, including experts with computer science backgrounds, to rewrite seven popular benchmarks, such as HumanEval and GSM8K.<n>Our findings reveal that textbfalmost all of these widely used models show significant brittleness and unfairness to queries in AAVE.
  arXiv  Detail & Related papers  (2024-10-14T18:44:23Z)
• The Struggles of LLMs in Cross-lingual Code Clone Detection [3.5202378300682162]
  Cross-lingual code clone detection has gained traction within the software engineering community.<n>Inspired by the significant advances in machine learning, this paper revisits cross-lingual code clone detection.<n>We evaluate the performance of five (05) Large Language Models (LLMs) and eight prompts (08) for the identification of cross-lingual code clones.
  arXiv  Detail & Related papers  (2024-08-08T12:57:14Z)
• Assessing Code Generation with Intermediate Languages [6.999311675957218]
  This study explores the utilization of intermediate languages, including various programming languages, natural language solutions, and pseudo-code. Our findings reveal that intermediate languages generally exhibit greater efficacy in larger models that have not yet achieved state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-07-07T15:35:41Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts.<n>We find that Llama Instruct and Mistral models exhibit high degrees of language confusion.<n>We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• Evaluating Neural Language Models as Cognitive Models of Language Acquisition [4.779196219827507]
  We argue that some of the most prominent benchmarks for evaluating the syntactic capacities of neural language models may not be sufficiently rigorous. When trained on small-scale data modeling child language acquisition, the LMs can be readily matched by simple baseline models. We conclude with suggestions for better connecting LMs with the empirical study of child language acquisition.
  arXiv  Detail & Related papers  (2023-10-31T00:16:17Z)
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
  We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
  arXiv  Detail & Related papers  (2023-09-29T17:57:00Z)
• Are Large Language Models Robust Coreference Resolvers? [17.60248310475889]
  We show that prompting for coreference can outperform current unsupervised coreference systems. Further investigations reveal that instruction-tuned LMs generalize surprisingly well across domains, languages, and time periods.
  arXiv  Detail & Related papers  (2023-05-23T19:38:28Z)
• Training Language Models with Language Feedback at Scale [50.70091340506957]
  We introduce learning from Language Feedback (ILF), a new approach that utilizes more informative language feedback. ILF consists of three steps that are applied iteratively: first, conditioning the language model on the input, an initial LM output, and feedback to generate refinements. We show theoretically that ILF can be viewed as Bayesian Inference, similar to Reinforcement Learning from human feedback.
  arXiv  Detail & Related papers  (2023-03-28T17:04:15Z)
• On Cross-Lingual Retrieval with Multilingual Text Encoders [51.60862829942932]
  We study the suitability of state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks. We benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR experiments. We evaluate multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to rank) on English relevance data in a series of zero-shot language and domain transfer CLIR experiments.
  arXiv  Detail & Related papers  (2021-12-21T08:10:27Z)
• From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension [51.953428342923885]
  We develop a two-stage approach to enhance the model performance. The first stage targets at recall: we design a hard-learning (HL) algorithm to maximize the likelihood that the top-k predictions contain the accurate answer. The second stage focuses on precision: an answer-aware contrastive learning mechanism is developed to learn the fine difference between the accurate answer and other candidates.
  arXiv  Detail & Related papers  (2021-12-09T07:31:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.