LLMs Explain't: A Post-Mortem on Semantic Interpretability in Transformer Models
This work highlights critical limitations in widely used interpretability methods, which is important for researchers and practitioners in pervasive computing who rely on such methods for debugging and explaining LLMs.
The study investigated how linguistic abstraction emerges in LLMs by applying established interpretability methods, but both probing for token-level structures and feature-mapping with embeddings failed due to methodological artifacts and dataset issues, showing that current techniques do not reliably explain LLM understanding.
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.