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Improving Data Efficiency in Fine-tuning Large Language Models with SMALLTOLARGE (S2L)

Core Concepts
SMALLTOLARGE (S2L) introduces a scalable data selection method for supervised fine-tuning, leveraging training trajectories from small models to guide data selection for larger models. It significantly improves data efficiency in specialized domains.
SMALLTOLARGE (S2L) is a novel approach that enhances data efficiency in supervised fine-tuning for large language models. By utilizing training trajectories from smaller models, S2L outperforms state-of-the-art algorithms across various datasets and domains, showcasing its effectiveness and scalability. The content discusses the challenges of data efficiency in supervised fine-tuning for specialized domains and introduces SMALLTOLARGE (S2L) as a solution. Through experiments on mathematical problem-solving and clinical text summarization tasks, S2L demonstrates superior performance by reducing the required training data size while maintaining or surpassing full dataset performance. Key points include the introduction of S2L as a scalable data selection method, its utilization of training trajectories from small models to guide data selection for larger models, and its significant improvements in data efficiency across different datasets and domains. The content also highlights the importance of balanced sampling from clusters to ensure coverage of all learning patterns. Additionally, ablation studies show that S2L is robust to the length of training trajectories but benefits more from longer trajectories. Denser trajectories recorded at any stage of training are preferred by S2L for optimal performance.
Selecting only 50K data points, S2L achieves a 32.7% accuracy on the MATH benchmark. In clinical text summarization on the MIMIC-III dataset, S2L outperforms training on the full dataset using only 50% of the data. S2L can perform data selection using a reference model 40× smaller than the target model.

Key Insights Distilled From

by Yu Yang,Sidd... at 03-13-2024
SmallToLarge (S2L)

Deeper Inquiries

How does S2L compare to traditional heuristic approaches like manual curation in terms of cost-effectiveness?

S2L outperforms traditional heuristic approaches like manual curation in terms of cost-effectiveness. Manual curation requires specialized knowledge and can be costly, especially with large volumes of uncurated fine-tuning data. On the other hand, S2L leverages training trajectories from smaller models to guide data selection for larger models. This method is more scalable and efficient as it reduces the need for expensive manual intervention and relies on automated processes based on training dynamics.

What implications does the use of smaller models for guiding data selection have on computational resources?

The use of smaller models for guiding data selection has significant implications on computational resources. By leveraging training trajectories from smaller models, such as Pythia-70M in the case of S2L, the computational requirements are reduced compared to using larger target models directly for feature representation generation. This approach allows for more efficient data selection without compromising performance while reducing costs associated with training and deploying large language models.

How might the principles behind S2L be applied to other domains beyond language modeling?

The principles behind S2L can be applied to various domains beyond language modeling by adapting the methodology to suit different types of datasets and tasks. For example: Image Recognition: Training trajectories from small image recognition models could guide data selection for larger image classification tasks. Healthcare: In medical imaging analysis, similar clustering techniques could help select relevant patient scans or pathology images efficiently. Financial Analysis: Utilizing trajectory-based clustering could aid in selecting historical financial data points that are most informative for predicting market trends. By applying similar concepts across diverse domains, researchers can enhance data efficiency, reduce computational costs, and improve model performance in specialized areas outside language modeling contexts.