Representing the Toddler Lexicon: Do the Corpus and Semantics Matter? (2022.lrec-1)
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| Challenge: | Existing studies on child language development have relied on adult-based measures to model their lexicons. |
| Approach: | They propose to use transcripts of child-directed conversations, picture books and dialog from G-rated movies to approximate the language input a North American preschooler might hear. |
| Outcome: | The proposed model outperforms models based on the existing corpus and the existing model. |
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| Challenge: | Large language models have been shown to be effective in creating educational tools for children, yet there are significant challenges in maintaining key child-specific properties such as linguistic nuances, cognitive needs, and safety standards. |
| Approach: | They propose a user-centric data collection pipeline that involves gathering and validating a corpus specifically written for and sometimes by children. |
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Is Child-Directed Speech Effective Training Data for Language Models? (2024.emnlp-main)
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| Challenge: | High-performing language models are typically trained on hundreds of billions of words, but human learners use language fluently after far less training data. |
| Approach: | They train GPT-2 and RoBERTa models on 29M words of English child-directed speech and a new matched, synthetic dataset. |
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Word Acquisition in Neural Language Models (2022.tacl-1)
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| Challenge: | Language models acquire individual words during training, based on unigram token frequencies, before transitioning loosely to bigram probabilities, eventually converging on more nuanced predictions. |
| Approach: | They examine how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Communicative Development Inventory. |
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Language acquisition: do children and language models follow similar learning stages? (2023.findings-acl)
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| Challenge: | During language acquisition, children follow a typical sequence of learning stages, whereby they first learn to categorize phonemes before they develop their lexicon and eventually master complex syntactic structures. |
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Small Language Models Also Work With Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas (2025.coling-main)
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| Challenge: | Existing studies on LMs have focused on linguistic generalizations and representations from developmentally plausible data. |
| Approach: | They propose to use phoneme- and grapheme-based language models to learn linguistic units at and below the word level. |
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Analyzing Vocabulary Commonality Index Using Large-scaled Database of Child Language Development (L18-1)
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| Challenge: | a vocabulary commonality index is used to investigate to what extent each child acquires common words during the early stages of lexical development. |
| Approach: | They propose a vocabulary commonality index to investigate to what extent each child acquires common words during the early stages of lexical development. |
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CHICA: A Developmental Corpus of Child-Caregiver’s Face-to-face vs. Video Call Conversations in Middle Childhood (2024.lrec-main)
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Dhia Elhak Goumri, Abhishek Agrawal, Mitja Nikolaus, Hong Duc Thang Vu, Kübra Bodur, Elias Emmar, Cassandre Armand, Chiara Mazzocconi, Shreejata Gupta, Laurent Prévot, Benoit Favre, Leonor Becerra-Bonache, Abdellah Fourtassi
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A Distributional Perspective on Word Learning in Neural Language Models (2025.naacl-long)
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| Challenge: | Language models are increasingly being studied as models of human language learners. |
| Approach: | They propose a distributional approach to word learning that captures distributional knowledge and gradient preferences for the word’s appropriateness. |
| Outcome: | The proposed signatures capture knowledge of where the target word can and cannot occur as well as gradient preferences about the word’s appropriateness. |
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines (N19-1)
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| Challenge: | Experimental results show that standalone n-gram models lend themselves as natural choices for resource-lean or morphologically rich languages. |
| Approach: | They run experiments on 50 languages covering all morphological language families to compare n-gram models with lstm models. |
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Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)
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| Challenge: | Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing. |
| Approach: | They propose a framework that assesses LLM-generated text based on semantic understanding. |
| Outcome: | The proposed framework surpasses traditional evaluation metrics and lags behind GPT-4. |