mlsanityData to 14 Oct 2026

Benchmarking LLMs for Mimicking Child-Caregiver Language in Interaction

arXiv 2412.09318 ↗First posted 12 Dec 2024cs.CL cs.AIcited by 6

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Abstract (arXiv)

LLMs can generate human-like dialogues, yet their ability to simulate early child-adult interactions remains largely unexplored. In this paper, we examined how effectively LLMs can capture the distinctive features of child-caregiver language in interaction, using both static and interactive benchmarking methods. We found that state-of-the-art LLMs like Llama 3 and GPT-4o can approximate child-caregiver dialogues at the word and utterance level, but they struggle to reproduce the child and caregiver's discursive patterns, exaggerate alignment, and fail to reach the level of diversity shown by humans. The broader goal of this work is to initiate the development of a comprehensive benchmark for LLMs in child-oriented applications.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 12 Dec 2024.

Builds on

9 of 29 references.

References · 9

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2020Recipes for building an open-domain chatbotcited by 593
  5. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 161
  6. 2023The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Textcited by 100
  7. 2023ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Eventscited by 31
  8. 2024Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytellingcited by 21
  9. 2024Can Generic LLMs Help Analyze Child-adult Interactions Involving Children with Autism in Clinical Observation?cited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)cited by 11
  2. 2025BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Datacited by 10
  3. 2026Evaluating Cognitive Age Alignment in Interactive AI Agentscited by 2
  4. 2026ChildEval: When large language models meet children's personalitiescited by 1
  5. 2025Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Studycited by 0
  6. 2026Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMscited by 0