Trustworthiness of Children Stories Generated by Large Language Models
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Abstract (arXiv)
Large Language Models (LLMs) have shown a tremendous capacity for generating literary text. However, their effectiveness in generating children's stories has yet to be thoroughly examined. In this study, we evaluate the trustworthiness of children's stories generated by LLMs using various measures, and we compare and contrast our results with both old and new children's stories to better assess their significance. Our findings suggest that LLMs still struggle to generate children's stories at the level of quality and nuance found in actual stories
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 25 Jul 2023.
Builds on
14 of 31 references.
References · 14
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
- 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
- 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
- 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 734
- 2021BARTScore: Evaluating Generated Text as Text Generationcited by 722
- 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 481
- 2023Nationality Bias in Text Generationcited by 50
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.