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instruction-following difficulty score

An instruction-following difficulty score is a quantitative metric used in machine learning to measure how challenging it is for a language model to generate a target response when prompted with a specific instruction. Rather than merely evaluating the intrinsic complexity of generating the output text on its own, the score assesses the discrepancy between the model probability of generating the response conditioned on the instruction and the probability of generating that response unconditionally or directly. By comparing these two values, typically through a ratio of cross-entropy loss or perplexity, the score determines whether an instruction effectively guides the model or presents a significant alignment hurdle. This metric is primarily applied during instruction tuning to identify, filter, and select the most informative, high-impact training samples, enabling more efficient model optimization using smaller, higher-quality datasets.

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From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

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

OrganizationsPing An Technology (Shenzhen) Co., Ltd.University of Maryland

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