Trustworthiness of Children Stories Generated by Large Language Models

Abstract

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. 1

Towards AI-Driven Healthcare: Systematic Optimization, Linguistic Analysis, and Clinicians’ Evaluation of Large Language Models for Smoking Cessation Interventions

“…For instance, in tasks where accuracy is paramount, such as summarization, translation, or message generation for a healthcare context, more restrictive decoding methods are often employed (e.g., k=2, as in [ 28 ]). Conversely, tasks prioritizing diversity, like creative writing, advertising copywriting, or storytelling, may benefit from more flexible decoding methods (e.g., k=100, as cited in [ 10 ]). Therefore, we suggest customizing decoding methods to align with the specific requirements of the NLP task in future studies.…”