LLMs Explain't: A Post-Mortem on Semantic Interpretability in Transformer Models

TL;DR

This paper critically examines the interpretability of LLMs, revealing that common explainability methods fail to reliably uncover semantic understanding, raising concerns about their use in practical applications.

Contribution

The study systematically tests popular interpretability techniques on LLMs and demonstrates their limitations, challenging assumptions about LLMs' semantic comprehension.

Findings

Probing methods failed to reveal meaningful token-level structures.

Embedding-based property inference was driven by artifacts, not semantics.

Common explainability techniques may not reliably reflect LLMs' understanding.

Abstract

Large Language Models (LLMs) are becoming increasingly popular in pervasive computing due to their versatility and strong performance. However, despite their ubiquitous use, the exact mechanisms underlying their outstanding performance remain unclear. Different methods for LLM explainability exist, and many are, as a method, not fully understood themselves. We started with the question of how linguistic abstraction emerges in LLMs, aiming to detect it across different LLM modules (attention heads and input embeddings). For this, we used methods well-established in the literature: (1) probing for token-level relational structures, and (2) feature-mapping using embeddings as carriers of human-interpretable properties. Both attempts failed for different methodological reasons: Attention-based explanations collapsed once we tested the core assumption that later-layer representations still correspond to tokens. Property-inference methods applied to embeddings also failed because their high predictive scores were driven by methodological artifacts and dataset structure rather than meaningful semantic knowledge. These failures matter because both techniques are widely treated as evidence for what LLMs supposedly understand, yet our results show such conclusions are unwarranted. These limitations are particularly relevant in pervasive and distributed computing settings where LLMs are deployed as system components and interpretability methods are relied upon for debugging, compression, and explaining models.