Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Study

• URL: http://arxiv.org/abs/2508.13769v1
• Date: Tue, 19 Aug 2025 12:13:54 GMT
• Title: Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Study
• Authors: Hanna Woloszyn, Benjamin Gagl,
• Abstract summary: This study evaluates how large language models (LLMs) replicate child-like language by comparing LLM-generated texts to a collection of German children's descriptions of picture stories.<n>We conducted a comparative analysis across psycholinguistic text properties, including word frequency, lexical richness, sentence and word length, part-of-speech tags, and semantic similarity with word embeddings.<n>The results show that LLM-generated texts are longer but less lexically rich, rely more on high-frequency words, and under-represent nouns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The role of large language models (LLMs) in education is increasing, yet little attention has been paid to whether LLM-generated text resembles child language. This study evaluates how LLMs replicate child-like language by comparing LLM-generated texts to a collection of German children's descriptions of picture stories. We generated two LLM-based corpora using the same picture stories and two prompt types: zero-shot and few-shot prompts specifying a general age from the children corpus. We conducted a comparative analysis across psycholinguistic text properties, including word frequency, lexical richness, sentence and word length, part-of-speech tags, and semantic similarity with word embeddings. The results show that LLM-generated texts are longer but less lexically rich, rely more on high-frequency words, and under-represent nouns. Semantic vector space analysis revealed low similarity, highlighting differences between the two corpora on the level of corpus semantics. Few-shot prompt increased similarities between children and LLM text to a minor extent, but still failed to replicate lexical and semantic patterns. The findings contribute to our understanding of how LLMs approximate child language through multimodal prompting (text + image) and give insights into their use in psycholinguistic research and education while raising important questions about the appropriateness of LLM-generated language in child-directed educational tools.

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