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The recent success of large language models relies heavily on extensive text datasets for pre-training. However, indiscriminate use of all available data may not be optimal due to varying quality. Data selection methods are crucial for optimizing training datasets and reducing costs and carbon footprint. Despite the expanding interest in this area, limited resources hinder extensive research. Consequently, effective data selection practices are concentrated within a few organizations, with findings often private.
Data selection in machine learning aims to optimize datasets, primarily enhancing model performance while addressing cost reduction, metric integrity, and mitigating biases. Data selection is pivotal in large language models across various training stages, like pretraining and fine-tuning. Filtering, web scraping, and quality assessment are commonly employed to curate high-quality data from a vast corpus.
The researchers from Massachusetts Institute of Technology, Stanford University, and others propose a conceptual framework to unify diverse data selection methods, particularly focusing on model pretraining. They emphasized the importance of understanding each method’s utility function and selection mechanism. By categorizing these methods and creating a taxonomy, they aim to offer a comprehensive resource on data selection practices for language model training.
They organized the survey as follows:
The taxonomy of data selection includes basic definitions of terms related to the dataset, such as data point, dataset, and dataset distribution.
A Unified Conceptual Framework for Data Selection discusses the definition of data selection and components of data selection methods (like selection mechanism)
Data Selection for Pretraining: The pretraining of a model is for general purposes, and later, the model can be fine-tuned on specific tasks. Hence, it requires a large amount of data. Selecting the best data from such large quantities can be very expensive. Therefore, a common first step in the process is to remove data with various filters, and multiple filters will likely need to be pipelined together to achieve the desired dataset. This paper includes language filtering, classifier-based quality filtering, and filtering toxic and explicit content, and it contains important filtering.
Data Selection for Preference Fine-tuning: Alignment, Various alignment methods, referred to under the umbrella of Reinforcement Learning from Human Feedback (RLHF), RL from AI Feedback (RLAIF), or Direct Preference Optimization (DPO) methods, involve the integration of human preferences into model behavior.
In conclusion, the researchers from Massachusetts Institute of Technology, Stanford University, and others have outlined a method for selecting datasets for large language models. They covered various aspects of data selection, including methods for decontaminating test sets, tradeoffs between memorization and generalization in model training, the impact of filtering strategies on model biases, and tools available for data exploration and selection. It emphasizes the importance of understanding and auditing datasets before applying selection mechanisms and highlights the availability of open-source tools for implementing data selection methods.
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“}]] [[{“value”:”The recent success of large language models relies heavily on extensive text datasets for pre-training. However, indiscriminate use of all available data may not be optimal due to varying quality. Data selection methods are crucial for optimizing training datasets and reducing costs and carbon footprint. Despite the expanding interest in this area, limited resources hinder
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