keyword
data quality
Data quality refers to the degree to which a dataset is accurate, complete, reliable, and fit for its intended use in operations, analysis, and computational modeling. It is commonly evaluated across core dimensions such as correctness, consistency, timeliness, completeness, and relevance to specific objectives. In data science and machine learning, data quality directly governs the performance, generalizability, and safety of trained models, where curated and representative samples enable effective pattern recognition while noisy, biased, or corrupted data degrades system outputs and leads to flawed decisions. Maintaining high data quality involves structured processes including data profiling, validation, deduplication, error correction, and selective filtering throughout the data lifecycle.
2 items
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao
Why you should read this
Proposes the Instruction-Following Difficulty metric to let large language models autonomously select high-impact training samples, outperforming full-dataset fine-tuning on Alpaca and WizardLM while using only ten percent of the data.
In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability. Through the application of IFD, cherry samples can be pinpointed, leading to a marked uptick in model training efficiency. Empirical validations on datasets like Alpaca and WizardLM underpin our findings; with a mere 10% of original data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the instruction tuning of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available.
Added
2026-09-28
Data Traps
Google for Developers
Why you should read this
Avoid costly ML failures by learning to identify subtle data quality issues, biases, and false inferences early—before you waste resources training models on flawed datasets.
Added
2025-09-22
