Year: 2025
Type: conference-paper
Abstract: 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. 1 * Equal contribution. 1 Our source code is publicly available at https://github.com/liyuqian44/ On-Support-Samples-of-Next-Word-Prediction.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412945448