Question-Answer Generation (QAG) is essential for alleviating the cold-start problem in domain-specific large language model (LLM) post-training, where high-quality data is severely scarce.Effective training samples include rich semantic diversity and rigorous factual consistency.Thus, it is necessary to consider the inherent tension between semantic breadth and factual fidelity.However, it is extremely challenging to trade off semantic diversity against factual consistency, in that generalization across the semantic space must be achieved effectively and reliably, and factual integrity must be ensured as well.To address this issue, we propose an effective framework, namely DiFRa, that integrates continuous concept diffusion with discrete knowledge graph constraints to balance semantic diversity and factual consistency.Specifically, the proposed DiFRa models discrete concepts as a continuous latent distribution to sample embeddings that capture rich semantic variations, and constructs a refined knowledge graph as explicit factual constraints.Then, a diversity and consistency aware mechanism is designed to dynamically integrate both embeddings and the knowledge graph for QA pairs generation.Furthermore, we introduce SeFa, which harmonizes semantic entropy and consistency scores to quantify the trade-off between diversity and correctness.Extensive experiments demonstrate that DiFRa consistently outperforms the baseline models, validating its efficacy in reconciling the tension to generate semantically diverse and factually consistent QA pairs. The source code is publicly available.

In recent years, driven by advancements in the diffusion process, Text-to-Image (T2I) models have rapidly developed. However, evaluating T2I models remains a significant challenge. While previous research has thoroughly assessed the quality of generated images and image-text alignment, there has been little study on the creativity of these models. In this work, we defined the creativity of T2I models, inspired by previous definitions of machine creativity. We also proposed corresponding metrics and designed a method to test the reliability of the metric. Additionally, we developed a fully automated pipeline capable of transforming existing image-text datasets into benchmarks tailored for evaluating creativity, specifically through text vector retrieval and the text generation capabilities of large language models (LLMs). Finally, we conducted a series of tests and analyses on the evaluation methods for T2I model creativity and the factors influencing the creativity of the models, revealing that current T2I models demonstrate a lack of creativity. The code and benchmark will be released.