**Abstract:** Large language models (LLMs) can solve diverse tasks when provided with appropriate natural language prompts, but this often results in lower accuracy compared to finetuning with ample training data. Finetuning LLMs on task-specific data improves performance but requires abundant annotated datasets, which are not always available. Previous work has explored generating task-specific data from LLMs and using it for finetuning, but this approach relies on additional powerful LLMs, introducing costs and scalability challenges. To address these issues, we propose SELF-GUIDE, a multi-stage mechanism that synthesizes task-specific input-output pairs from the student LLM and uses these pairs to finetune the student LLM itself. Empirical evaluation on the Natural Instructions V2 benchmark shows that SELF-GUIDE improves LLM performance by approximately 15% for classification tasks and 18% for generation tasks, demonstrating the potential of self-synthesized data in guiding LLMs towards task-specific expertise without external learning signals. **Introduction:** This paper addresses the challenge of improving LLMs' performance on specific tasks with minimal annotated data. SELF-GUIDE operates in a few-shot setting, where the model is given a task instruction and up to three examples. It generates synthetic input-output pairs and then finetunes the model on this self-generated data. Unlike previous methods that use a base LLM to generate synthetic instructions, SELF-GUIDE aims to optimize the student LLM for a specific task instruction, generating hundreds of examples for each instruction. Empirical results show significant improvements in performance, highlighting the effectiveness of self-synthesized data in adapting LLMs for specialized tasks. **SELF-GUIDE:** SELF-GUIDE involves multiple stages, including input generation, output generation, and quality optimization. Input generation extracts inputs from example pairs and combines them with the instruction to generate new inputs. Output generation uses in-context learning techniques to generate annotated outputs. Quality optimization adjusts parameters like temperature and applies rule-based filters to improve data quality. **Experimental Setup:** The effectiveness of SELF-GUIDE is evaluated on 14 classification tasks and 8 generation tasks from the Super-NaturalInstructions V2 benchmark. The base model used is Vicuna-7b-1.5, and the evaluation metrics are Exact Match for classification and ROUGE-L for generation.SELF-GUIDE outperforms baselines such as few-shot prompting and in-context learning, demonstrating its ability to leverage synthetic data effectively. **Results and Analysis:** SELF-GUIDE significantly improves performance on both classification and generation tasks, reducing the proportion of irrelevant outputs and aligning the model's output distribution with the ground truth. Ablation studies show that the noise filter is crucial for classification tasks, while the length filter is essential for generation tasks. **Conclusion:** SELF-GUIDE demonstrates the potential of self-synthesized data in enhancing LLMs' task-specific expertise, particularly in data**Abstract:** Large language models (LLMs) can solve diverse tasks when provided with appropriate natural language prompts, but this often results in lower accuracy compared to finetuning with ample training data. Finetuning LLMs on task-specific data improves performance but requires abundant annotated datasets, which are not always available. Previous work has explored generating task-specific data from LLMs and using it for finetuning, but this approach relies on additional powerful LLMs, introducing costs and scalability challenges. To address these issues, we propose SELF-GUIDE, a multi-stage mechanism that synthesizes task-specific input-output pairs from the student LLM and uses these pairs to finetune the student LLM itself. Empirical evaluation on the Natural Instructions V2 benchmark shows that SELF-GUIDE improves LLM performance by approximately 15% for classification tasks and 18% for generation tasks, demonstrating the potential of self-synthesized data in guiding LLMs towards task-specific expertise without external learning signals. **Introduction:** This paper addresses the challenge of improving LLMs' performance on specific tasks with minimal annotated data. SELF-GUIDE operates in a few-shot setting, where the model is given a task instruction and up to three examples. It generates synthetic input-output pairs and then finetunes the model on this self-generated data. Unlike previous methods that use a base LLM to generate synthetic instructions, SELF-GUIDE aims to optimize the student LLM for a specific task instruction, generating hundreds of examples for each instruction. Empirical results show significant improvements in performance, highlighting the effectiveness of self-synthesized data in adapting LLMs for specialized tasks. **SELF-GUIDE:** SELF-GUIDE involves multiple stages, including input generation, output generation, and quality optimization. Input generation extracts inputs from example pairs and combines them with the instruction to generate new inputs. Output generation uses in-context learning techniques to generate annotated outputs. Quality optimization adjusts parameters like temperature and applies rule-based filters to improve data quality. **Experimental Setup:** The effectiveness of SELF-GUIDE is evaluated on 14 classification tasks and 8 generation tasks from the Super-NaturalInstructions V2 benchmark. The base model used is Vicuna-7b-1.5, and the evaluation metrics are Exact Match for classification and ROUGE-L for generation.SELF-GUIDE outperforms baselines such as few-shot prompting and in-context learning, demonstrating its ability to leverage synthetic data effectively. **Results and Analysis:** SELF-GUIDE significantly improves performance on both classification and generation tasks, reducing the proportion of irrelevant outputs and aligning the model's output distribution with the ground truth. Ablation studies show that the noise filter is crucial for classification tasks, while the length filter is essential for generation tasks. **Conclusion:** SELF-GUIDE demonstrates the potential of self-synthesized data in enhancing LLMs' task-specific expertise, particularly in data