Quantifying the Importance of Data Alignment in Downstream Model Performance

• URL: http://arxiv.org/abs/2501.08496v2
• Date: Tue, 21 Jan 2025 01:01:37 GMT
• Title: Quantifying the Importance of Data Alignment in Downstream Model Performance
• Authors: Krrish Chawla, Aryan Sahai, Mario DePavia, Sudharsan Sundar, Brando Miranda,
• Abstract summary: We use the Task2Vec-based alignment coefficient to quantify the impact of alignment between training data and evaluation data on downstream performance. We find a strong, predictable negative correlation between the alignment coefficient of a model's training and evaluation data and the model's loss/perplexity on the respective downstream task.
• Score: 1.2564343689544843
• License:
• Abstract: Contrary to the conventional emphasis on dataset size, we explore the role of data alignment -- an often overlooked aspect of data quality -- in training capable Large Language Models (LLMs). To do so, we use the Task2Vec-based alignment coefficient, a quantitative measure of the similarity between two datasets, to quantify the impact of alignment between training data and evaluation data on downstream performance. In particular, we conduct controlled \textit{interventional} experiments for two settings: 1. the impact of increased alignment coefficients between various pre-training (pt) against evaluation datasets, and 2. the impact of increased alignment coefficients between domain specific fine-tuning (ft) against domain specific evaluation. The domain specific task we explore is Autoformalization -- the machine translation task between natural language and code for formal verification. In both settings, we find a strong, predictable negative correlation between the alignment coefficient of a model's training and evaluation data and the model's loss/perplexity on the respective downstream task. These findings suggest a re-evaluation of LLM training approaches, demonstrating the relevance of data alignment compared to data quantity, especially in specialized downstream tasks such as Autoformalization.

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