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Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
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Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions. While instruction tuning offers advantages for facilitating the adaptation of large language models (LLMs) to downstream tasks as a fine-tuning approach, training models with tens of millions or even billions of parameters on large amounts of data results in unaffordable computational costs. To address this, we focus on reducing the data used in LLM instruction tuning to decrease training costs and improve data efficiency, dubbed as Low Training Data Instruction Tuning (LTD Instruction Tuning). Specifically, this paper conducts a preliminary exploration into reducing the data used in LLM training and identifies several observations regarding task specialization for LLM training, such as the optimization of performance for a specific task, the number of instruction types required for instruction tuning, and the amount of data required for task-specific models. The results suggest that task-specific models can be trained using less than 0.5% of the original dataset, with a 2% improvement in performance over those trained on full task-related data.
Forward citations
Cited by 7 Pith papers
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MFT, a corrective self-distillation objective, reports 2 to 10 times better specialization-to-degeneralization ratios than standard finetuning across many models and three specialized domains.
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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models
ALPS selects task-sensitive attention heads by measuring Wasserstein distance between base and task-tuned weights, and freezing other heads during fine-tuning improves performance and efficiency.
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ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation
ReqBrain, a LoRA-fine-tuned Zephyr-7b-beta model, produces software requirements that human evaluators could not reliably tell apart from human-authored ones, with automatic metrics favoring it over untuned ChatGPT-4o.
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A literature survey that classifies LLM-based optimization research into modeling and solving, with solving divided into LLMs as optimizers, low-level components, and high-level managers.
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