Do Language Models Encode Semantic Relations? Probing and Sparse Feature Analysis
Understanding whether large language models (LLMs) capture structured meaning requires examining how they represent concept relationships. In this work, we study three models of increasing scale: Pythia-70M, GPT-2, and Llama 3.1 8B, focusing on four semantic relations: synonymy, antonymy, hypernymy, and hyponymy. We combine linear probing with mechanistic interpretability techniques, including sparse autoencoders (SAE) and activation patching, to identify where these relations are encoded and how specific features contribute to their representation. Our results reveal a directional asymmetry in hierarchical relations: hypernymy is encoded redundantly and resists suppression, while hyponym...
| Authors | Andor Diera, Ansgar Scherp |
|---|---|
| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 31 March 2026 |
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