Data Efficacy: Optimizing LLM Training

Data Efficacy vs. Efficiency

👋 Welcome to this month’s edition of State of AI: Monthly Paper Deep Dive.

Each month, we break down one standout AI research paper, explaining it clearly and concisely for ML engineers and research scientists. Today’s focus is a research paper introducing DELT – a new paradigm that organizes training data for better language model performance.

Let’s dive in.

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🔍 Introduction

Until now, most LLM training optimization has focused on data efficiency: finding the best subset of data to train on. That includes:

• Filtering poor quality or duplicate data

• Sampling the most informative examples

• Removing noisy, misleading examples

But this paper asks:

What if how we order and present data to the model is as important as which data we choose?