On Support Samples of Next Word Prediction

Language models excel in various tasks by making complex decisions, yet understanding the rationale behind these decisions remains a challenge.This paper investigates data-centric interpretability in language models, focusing on the next-word prediction task.Using representer theorem, we identify two types of support samples-those that either promote or deter specific predictions.Our findings reveal that being a support sample is an intrinsic property, predictable even before training begins.Additionally, while non-support samples are less influential in direct predictions, they play a critical role in preventing overfitting and shaping generalization and representation learning.Notably, the importance of non-support samples increases in deeper layers, suggesting their significant role in intermediate representation formation.These insights shed light on the interplay between data and model decisions, offering a new dimension to understanding language model behavior and interpretability.