Children’s stories play a crucial role in early cognitive and language development, yet existing story generation models lack the safety and age-appropriateness required for educational applications, especially in non-English languages. We introduce CCStories, a curated dataset of 22,200 high-quality Chinese children’s stories designed specifically to support safe and appropriate AI-generated content for young readers. Unlike general-purpose datasets derived from adult-oriented platforms, CCStories targets three critical dimensions of child-appropriate text: readability, syntactic complexity, and toxicity. We further propose a multi-dimensional evaluation framework that jointly assesses appropriateness and generation quality. As a validation of the dataset’s utility, we fine-tune five representative backbones: CPM, PanGu-𝛼, GLM-4, Qwen, a Chinese-adapted LLaMA-3 variant (Gigi-Llama-3-8B-zh), and additionally benchmark GPT-5-mini as a zero-shot instruction-tuned reference. Fine-tuning reduces offensive-prompt toxicity by up to 26.7 percentage points and raises readability by up to 14.3 percentage points, narrowing much of the gap to the GPT-5-mini reference. A human evaluation confirms that appropriateness rises from 53% to 86% after fine-tuning, and human validation of the toxicity classifier shows that the model ranking it induces matches the human ranking. CCStories provides a publicly available resource for building and benchmarking safer, more age-appropriate Chinese generative models for children’s education.

Open domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language processing (NLP). Recent works have predominantly focused on improving the answering accuracy and have achieved promising progress. However, higher accuracy often requires more memory consumption and inference latency, which might not necessarily be efficient enough for direct deployment in the real world. Thus, a trade-off between accuracy, memory consumption and processing speed is pursued. In this paper, we will survey recent advancements in the efficiency of ODQA models and conclude core techniques for achieving efficiency. Additionally, we will provide a quantitative analysis of memory cost, query speed, accuracy, and overall performance comparison. Our goal is to keep scholars informed of the latest advancements and open challenges in ODQA efficiency research and contribute to the further development of ODQA efficiency.