Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance
arXiv:2605.31304v1 Announce Type: new Abstract: Deep neural networks (DNNs) are widely used, but interpreting what they actually learn remains difficult. A major obstacle is that individual neurons often encode multiple unrelated concepts, obscuring the decision process of the network. While prior work, such as sparse autoencoders, can separate these mixed signals into more meaningful, "monosemantic" features, this typically requires altering the model in ways that can degrade downstream performance. To overcome this, we introduce ELUDe (explicit, lossless, unsupervised disentanglement), a met
The increasing complexity and opacity of deep neural networks necessitate advanced interpretability methods to ensure reliability, safety, and regulatory compliance, particularly as AI integrates into critical systems.
Improving the interpretability of AI models without sacrificing performance is crucial for unlocking broader adoption, enabling debugging, fostering trust, and adhering to future AI governance frameworks.
The ability to 'disentangle polysemanticity' meaning individual neurons being responsible for one thing changes the landscape of what is possible regarding explainable AI.
- · AI developers
- · AI ethicists
- · Regulatory bodies
- · Industries deploying AI in critical applications
- · Black-box AI models
- · AI systems lacking transparency
More transparent and debuggable AI models become widely accessible across various applications.
Increased trust in AI leads to faster adoption in sensitive domains such as healthcare and finance.
New regulatory standards emerge that mandate specific levels of AI interpretability, fostering a competitive advantage for developers using methods like ELUDe.
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Read at arXiv cs.LG