How to Train Data-Efficient LLMs (2402.09668v1)

Abstract: The training of LLMs is expensive. In this paper, we study data-efficient approaches for pre-training LLMs, i.e., techniques that aim to optimize the Pareto frontier of model quality and training resource/data consumption. We seek to understand the tradeoffs associated with data selection routines based on (i) expensive-to-compute data-quality estimates, and (ii) maximization of coverage and diversity-based measures in the feature space. Our first technique, Ask-LLM, leverages the zero-shot reasoning capabilities of instruction-tuned LLMs to directly assess the quality of a training example. To target coverage, we propose Density sampling, which models the data distribution to select a diverse sample. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories. Coverage sampling can recover the performance of the full data, while models trained on Ask-LLM data consistently outperform full-data training -- even when we reject 90% of the original dataset, while converging up to 70% faster.

• The paper introduces Ask-LLM sampling to prioritize high-quality training data, achieving up to 90% data reduction without compromising performance.
• The paper rigorously benchmarks 19 sampling strategies, revealing key trade-offs between data quality, diversity, and computational cost.
• The paper shows that efficient data selection methods significantly speed up model convergence, with improvements of up to 70% in training efficiency.

Optimizing LLM Training: Advances in Data Efficiency

Introduction to Data Efficiency in LLMs

The efficiency of training LLMs stands as a critical concern within the machine learning community, given the substantial computational resources necessary for processing extensive data volumes. This paper explores innovative strategies aimed at enhancing the data efficiency of pre-training LLMs, focusing on optimizing the trade-offs between model quality and consumption of data and computational resources. The researchers introduce two primary techniques: Ask-LLM for assessing the quality of training examples and Density sampling for promoting diversity in the training data. Through a comprehensive evaluation, including 19 distinct data samplers and extensive downstream task performance assessment, the paper elucidates the superiority of these methods in improving data utilization efficiency.

Key Contributions

The paper's contributions are manifold, presenting novel sampling methods and providing deep insights into the trade-offs and considerations in data-efficient LLM training:

• Ask-LLM Sampling emerges as a remarkably effective technique, capable of enhancing model performance even when discarding up to 90% of the training data. This method involves using a smaller proxy LLM to evaluate and prioritize high-quality training examples.
• Exhaustive Benchmarking of 19 sampling strategies offers a comprehensive overview of their comparative efficacy across a spectrum of downstream tasks, bringing valuable insights into the varying roles of coverage, quality, and sampling cost in LLM pre-training.
• New Insights into the dynamics of coverage versus quality in data selection are meticulously analyzed. The interplay between these factors highlights distinct advantages and demonstrates under which circumstances each approach yields the most substantial benefits.

Methodological Overview

Ask-LLM Sampling

The Ask-LLM technique represents a significant shift towards leveraging the inherent reasoning capabilities of instruction-tuned LLMs to ascertain the instructional quality of training data. This approach not only facilitates the identification of high-impact training examples but also speeds up the convergence time by up to 70%.

Density Sampling

Density sampling introduces an innovative approach to maximizing the diversity of training data. By modeling the data distribution, this technique effectively selects a varied sample that broadens the coverage of latent topics within the training dataset.

Experimental Insights

The experimental findings are revealing, suggesting distinct advantages in employing LLM-based quality rating for data selection:

• Performance Benefits: Models trained on Ask-LLM selected data consistently outperform those trained on the entirety of the dataset, showcasing the effectiveness of quality-focused data pruning.
• Data Reduction without Performance Loss: Remarkably, the Ask-LLM method enables training LLMs with significantly reduced datasets—rejecting up to 90% of the data—while maintaining or even improving model performance.
• Rapid Convergence: The rate of model convergence is notably accelerated when training on Ask-LLM filtered data, presenting a compelling case for its practical application in LLM training routines.

Implications and Future Directions

This research presents a leap forward in the pursuit of data-efficient LLM pre-training methodologies. It opens avenues for more sustainable and cost-effective LLM development by underscoring the possibility of reducing data requirements without compromising on model quality. Future explorations may explore refining LLM-based quality scoring mechanisms and expanding the application of these techniques to broader contexts in AI training paradigms. The promising outcomes of the Ask-LLM and Density sampling methods indicate a substantial potential for not only mitigating the computational intensity of LLM training but also for enhancing the overall quality and efficiency of generative AI models.

Conclusions

This paper asserts the substantial benefits of targeted data selection strategies in training more efficient and potent LLMs. By prioritizing data quality and diversity through advanced sampling techniques, it is possible to significantly improve the efficiency of the training process. The success of the Ask-LLM and Density sampling methods presents an exciting frontier in the quest for more sustainable and effective AI model training, promising considerable reductions in computational demands while elevating model performance.

Acknowledgements and Impact

The paper concludes by acknowledging the collaborative efforts and contributions to its research, while also contemplating the broader impact of data-efficient LLM pre-training. The improvements in training efficiency not only hold potential for economic and environmental benefits but also chart a course towards more accessible and scalable AI technologies.