F i NE : Filtering and Improving Noisy Data Elaborately with Large Language Models (2025)

Abstract: Data is the lifeblood of large language models (LLMs). While the quantity of open-source data available for training LLMs is substantial, its integrity often falls short. For instance, the open-source chat version of Yi-1.5-9B scores 5.20 on AlignBench, while the Chinese Alpaca-GPT4 version scores 4.12. This discrepancy makes it challenging for developers to create models that excel in downstream tasks and instruction following. Therefore, it is essential to improve data integrity. Currently, there are two mainstream methods for enhancing data integrity: data filtering and data augmentation. Due to the labor-intensive and time-consuming nature of performing these tasks manually, some of these efforts are now being undertaken by LLMs, owing to their high alignment with human preferences. However, we have found that performing data filtering or data augmentation with LLMs has limited effectiveness in improving data integrity. In this work, we propose FiNE ( F iltering and I mproving N oisy data E laborately), a method that performs refined filtering and improvement of training data with LLMs. Using the data obtained through our method to train Yi-1.5-9B, the performance gap on AlignBench between our model and the open-source chat version is reduced from 1.08 to 0.35. Additionally, on HalluQA, our model surpasses the open-source chat version by 8.45.

## introduction large language models (llms), such as chatgpt foot_1 , @xcite , and claude3 @xcite , have become the predominant approach in natural language processing due to their ## related work there are three crucial evaluation criteria for data integrity: quality, diversity, and importance (i.e. necessity) @xcite . ## methodology since necessity is model-dependent, fine focuses primarily on data quality and diversity. fine consists of three main steps. in the first two steps, we use a filter-then-augment approach to remove lowquality data and then improve the rest. in the third step, we use an augment-then-filter approach to increase data diversity while preventing duplicate data. ## ablation effectiveness of each component. we conducted detailed ablation experiments to assess the effectiveness of each fine component. ## conclusion in this work, we propose a method to improve data integrity by combining data filtering and augmentation using large language models focusing on data quality and diversity. initially, we employ a filter-then-augment approach, where low-quality noisy data is filtered based on meticulously designed metrics across multiple factors, followed by the remaining data quality improvement. subsequently, we adopt an augment-then-filter strategy, identifying low-diversity noisy data first and then compensating for the diversity lost during filtering while simultaneously preventing redundancy in the data generated by the llm during augmentation. when applied to alpaca-gpt4 and firefly to fine-tune llms, our method significantly improves their instruction-following capabilities and truthfulness, narrowing the gap with their corresponding open-source chat versions. this method and dataset enable downstream developers to create llms that perform well on downstream tasks and possess good instruction-following ability.