Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddings

TL;DR

This paper critically examines the common practice of interpreting word embeddings through feature prediction, revealing that such methods often reflect geometric similarity rather than genuine semantic understanding.

Contribution

It demonstrates that feature prediction methods do not reliably indicate semantic knowledge in embeddings and are influenced by geometric properties, challenging prior interpretability assumptions.

Findings

Prediction accuracy does not imply semantic interpretability.

Methods can predict random information, indicating superficial correlations.

Geometric similarity dominates the interpretability signals.

Abstract

Understanding what knowledge is implicitly encoded in deep learning models is essential for improving the interpretability of AI systems. This paper examines common methods to explain the knowledge encoded in word embeddings, which are core elements of large language models (LLMs). These methods typically involve mapping embeddings onto collections of human-interpretable semantic features, known as feature norms. Prior work assumes that accurately predicting these semantic features from the word embeddings implies that the embeddings contain the corresponding knowledge. We challenge this assumption by demonstrating that prediction accuracy alone does not reliably indicate genuine feature-based interpretability. We show that these methods can successfully predict even random information, concluding that the results are predominantly determined by an algorithmic upper bound rather than meaningful semantic representation in the word embeddings. Consequently, comparisons between datasets based solely on prediction performance do not reliably indicate which dataset is better captured by the word embeddings. Our analysis illustrates that such mappings primarily reflect geometric similarity within vector spaces rather than indicating the genuine emergence of semantic properties.