TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning

2025-08-27

AI Summary

Existing instruction data augmentation methods often neglect task relevance, struggling to balance diversity with scenario-specific adaptability. To address this, we propose Task-Centered Instruction Augmentation (TCIA), the first framework to incorporate a task-alignment mechanism into instruction generation. TCIA models a discrete query-constraint space to enable task-directed instruction expansion and filtering. By explicitly aligning generated instructions with target task semantics, TCIA preserves lexical and structural diversity while substantially improving task generalization—without compromising general instruction-following capability. Evaluated across four real-world task domains, TCIA boosts performance of open-source large language models by an average of 8.7% relative to strong baselines; in several metrics, it even surpasses leading proprietary models. These results demonstrate TCIA’s effectiveness in domain-specialized settings and its compatibility with broad-purpose instruction tuning.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Diverse instruction data is vital for effective instruction tuning of large language models, as it enables the model to generalize across different types of inputs . Building such diversified instruction dataset is an essential step in this process. Existing approaches often leverage large language models to automatically explore and generate diverse instructions, ensuring both data diversity and quality. However, they tend to overlook an important factor in real-world applications: on-task relevance. In practice, only a few real-world applications require a truly general-purpose model; most benefit from task-specific knowledge tailored to their particular use case. Therefore, it is vital to develop instruction augmentation methods that not only maintain diversity but are also optimized for specific, real-world scenarios. We thus introduce Task Centric Instruction Augmentation (TCIA), a framework that systematically expands instructions while preserving both diversity and task alignment. By representing instructions in a discrete query-constraints space, TCIA creates a rich set of task-relevant instructions and enables models to generalize to these task-specific instructions without sacrificing overall performance. Experiments show that TCIA improves open-source LLMs' performance by an average of 8.7% across four real-world, task-specific applications, and in some cases outperforming leading closed-source models. These improvements do not compromise general instruction-following ability, making TCIA a scalable and efficient solution for adapting LLMs to real-world, task-focused applications.

Problem

• Augmenting instruction data with task-specific relevance
• Ensuring diversity while maintaining task alignment
• Improving LLM performance for real-world applications

Innovation

• Task-centric instruction augmentation method
• Discrete query-constraints space representation
• Maintains diversity and task alignment