arXiv:2602.13524v2 Announce Type: replace Abstract: Identifying feature representations in language models is a central task in mechanistic interpretability. Several recent studies have made the observation that feature representations can be inferred in some cases from singular vectors of attention matrices. However, sound justification for this phenomenon is lacking. In this paper we address that question, asking: why and when do singular vectors align with features? First, we demonstrate that singular vectors robustly align with features in a model where features can be directly observed. W
The increasing complexity of large language models and the push for interpretability are driving this research to understand their internal mechanisms.
This research provides a deeper understanding of how AI models represent information, which is crucial for building more reliable, controllable, and explainable AI systems.
The ability to link singular vectors directly to feature alignment offers a more robust methodology for mechanistic interpretability within language models.
- · AI researchers
- · AI safety community
- · Developers of interpretable AI
- · Companies using LLMs in critical applications
- · Black-box AI approaches
Improved understanding of model internals will lead to more targeted interventions and debugging of AI systems.
Enhanced interpretability could accelerate the development of more robust AI agents and reduce deployment risks in sensitive applications.
A clearer picture of AI's internal reasoning may inform future regulatory frameworks for AI accountability and trust.
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