SELF-GUIDE is a method for large language models (LLMs) to improve their ability to follow task-specific instructions by generating synthetic training data and fine-tuning on this data. The approach involves using the model itself to generate input-output pairs, which are then used to further train the model. This method is particularly effective in scenarios where labeled data is scarce, as it allows the model to learn from self-generated examples without relying on external data sources. The SELF-GUIDE framework consists of multiple stages, including data generation and quality optimization. During data generation, the model is first used to create input-output pairs based on given instructions and examples. These pairs are then refined through rule-based filters to ensure quality. The quality optimization process involves adjusting generation parameters such as temperature and applying filters to remove low-quality examples. The method has been evaluated on various tasks, including classification and generation, and has shown significant improvements in performance compared to traditional prompting and fine-tuning methods. Specifically, SELF-GUIDE achieved an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics. The effectiveness of SELF-GUIDE is attributed to its ability to generate high-quality synthetic data that aligns with the desired distribution specified by the task. This data is then used to fine-tune the model, leading to improved performance on specific tasks. Additionally, the method has been shown to be more effective than in-context learning in leveraging synthetic data, as it allows the model to learn from a larger and more diverse set of examples. The approach also includes filters to ensure the generated data is relevant and of high quality. These filters help remove noise and irrelevant content, ensuring that the model learns from meaningful examples. The use of these filters is crucial for tasks such as classification, where the model must accurately distinguish between different labels. Overall, SELF-GUIDE demonstrates the potential of self-synthesized data in improving the performance of LLMs on specific tasks. By leveraging the model's ability to generate synthetic data, the method provides a scalable and efficient solution for improving task-specific instruction following without the need for external training signals.SELF-GUIDE is a method for large language models (LLMs) to improve their ability to follow task-specific instructions by generating synthetic training data and fine-tuning on this data. The approach involves using the model itself to generate input-output pairs, which are then used to further train the model. This method is particularly effective in scenarios where labeled data is scarce, as it allows the model to learn from self-generated examples without relying on external data sources. The SELF-GUIDE framework consists of multiple stages, including data generation and quality optimization. During data generation, the model is first used to create input-output pairs based on given instructions and examples. These pairs are then refined through rule-based filters to ensure quality. The quality optimization process involves adjusting generation parameters such as temperature and applying filters to remove low-quality examples. The method has been evaluated on various tasks, including classification and generation, and has shown significant improvements in performance compared to traditional prompting and fine-tuning methods. Specifically, SELF-GUIDE achieved an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics. The effectiveness of SELF-GUIDE is attributed to its ability to generate high-quality synthetic data that aligns with the desired distribution specified by the task. This data is then used to fine-tune the model, leading to improved performance on specific tasks. Additionally, the method has been shown to be more effective than in-context learning in leveraging synthetic data, as it allows the model to learn from a larger and more diverse set of examples. The approach also includes filters to ensure the generated data is relevant and of high quality. These filters help remove noise and irrelevant content, ensuring that the model learns from meaningful examples. The use of these filters is crucial for tasks such as classification, where the model must accurately distinguish between different labels. Overall, SELF-GUIDE demonstrates the potential of self-synthesized data in improving the performance of LLMs on specific tasks. By leveraging the model's ability to generate synthetic data, the method provides a scalable and efficient solution for improving task-specific instruction following without the need for external training signals.