Generating timely and accurate content is a significant challenge for Large Language Models (LLMs). Obsolete information reduces their reliability and user trust. To overcome the limitations of single models in adapting to evolving information, we propose a dynamic switching model. A multitask trained switch model objective, adaptively picks between a large model that does not have recent information and a smaller model fine-tuned on recent information using contextual and temporal indicators. This method incorporates semantic update detection and temporal switching, which predicts text obsolescence through aggregation of reward signals. For evaluation, we curated the Temporally-aware Dynamic Dataset (TaDD) on Wikipedia and Guardian articles, which are frequently updated. Our framework achieves a balanced precision-recall trade-off on five datasets without continuous retraining, which shows that the model is efficient and adaptable compared to static pretrained models.

Humor generation remains a challenging problem for language models due to its subjective, creative, and culturally sensitive nature. While large language models (LLMs) demonstrate strong generative capabilities, their deployment is often constrained by computational cost. We propose a lightweight, corrective feedback-driven framework in which a compact generator (GPT-2, 140M parameters) produces punchlines under the guidance of an LLM acting as a critic. Unlike traditional knowledge distillation, our method leverages the LLM as a feedback provider rather than a teacher to be mimicked, enabling the student model to iteratively refine its outputs through corrective supervision. We systematically evaluate multiple LLMs (LLaMA3-8B, Qwen3-8B and Mistral-7B) as guides under different strategies, including frozen and learnable configurations, to study their impact on humor quality and diversity. To support responsible humor generation, we curate a high-quality setup–punchline dataset from Reddit and explicitly filter out harmful content. Experimental results show that corrective feedback-guided training substantially improves humor quality, fluency, and diversity over standard fine-tuning and knowledge distillation baselines, while preserving the efficiency of compact models. We observe that, among the evaluated guides, Mistral-7B consistently provides particularly strong guidance on the curated joke dataset. Our code and resources are available at: https://github.com/Ramakrishna176420/Corrective-Feedback-Driven-Humor-Generation-for-Tiny-Language-Models.