This paper introduces CLEAR, an automated data curation pipeline for instruction tuning datasets that improves the quality of training data for large language models (LLMs) without requiring additional fine-tuning computations. CLEAR consists of two stages: Auto-Filter and Auto-Correct. Auto-Filter removes low-quality data based on confidence estimates, while Auto-Correct revises certain examples using the fine-tuned LLM to produce better responses. The pipeline uses a confidence-based response quality evaluator, BSDetector, to estimate the confidence that a response is good. This approach is more precise than conventional LLM scoring of response quality. Experiments show that CLEAR consistently improves the performance of fine-tuned models across various datasets and models, including GPT-3.5 and Llama2. The study also highlights the importance of data-centric approaches in AI, emphasizing that even the most advanced LLMs may struggle with specific domain challenges. CLEAR is designed to work with any LLM and fine-tuning algorithm, making it a versatile tool for improving instruction tuning datasets. The results demonstrate that data curation can significantly enhance model performance, even without additional fine-tuning. The paper also discusses the limitations of the approach, including potential biases in the original dataset and the need for further research to address these issues. Overall, CLEAR provides a comprehensive solution for improving the quality of instruction tuning datasets, leading to better fine-tuned LLMs.This paper introduces CLEAR, an automated data curation pipeline for instruction tuning datasets that improves the quality of training data for large language models (LLMs) without requiring additional fine-tuning computations. CLEAR consists of two stages: Auto-Filter and Auto-Correct. Auto-Filter removes low-quality data based on confidence estimates, while Auto-Correct revises certain examples using the fine-tuned LLM to produce better responses. The pipeline uses a confidence-based response quality evaluator, BSDetector, to estimate the confidence that a response is good. This approach is more precise than conventional LLM scoring of response quality. Experiments show that CLEAR consistently improves the performance of fine-tuned models across various datasets and models, including GPT-3.5 and Llama2. The study also highlights the importance of data-centric approaches in AI, emphasizing that even the most advanced LLMs may struggle with specific domain challenges. CLEAR is designed to work with any LLM and fine-tuning algorithm, making it a versatile tool for improving instruction tuning datasets. The results demonstrate that data curation can significantly enhance model performance, even without additional fine-tuning. The paper also discusses the limitations of the approach, including potential biases in the original dataset and the need for further research to address these issues. Overall, CLEAR provides a comprehensive solution for improving the quality of instruction tuning datasets, leading to better fine-tuned LLMs.