Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders
This work addresses the challenge of interpretability in AI systems, providing a foundational step with theoretical guarantees for more transparent and trustworthy models, though it is incremental in advancing existing SAE methods.
The paper tackled the problem of achieving theoretically grounded feature recovery in Large Language Models using Sparse Autoencoders, by proposing a statistical framework and a bias adaptation algorithm that provably recovers monosemantic features and demonstrates superior performance on models with up to 1.5 billion parameters.
We study the challenge of achieving theoretically grounded feature recovery using Sparse Autoencoders (SAEs) for the interpretation of Large Language Models. Existing SAE training algorithms often lack rigorous mathematical guarantees and suffer from practical limitations such as hyperparameter sensitivity and instability. To address these issues, we first propose a novel statistical framework for the feature recovery problem, which includes a new notion of feature identifiability by modeling polysemantic features as sparse mixtures of underlying monosemantic concepts. Building on this framework, we introduce a new SAE training algorithm based on ``bias adaptation'', a technique that adaptively adjusts neural network bias parameters to ensure appropriate activation sparsity. We theoretically \highlight{prove that this algorithm correctly recovers all monosemantic features} when input data is sampled from our proposed statistical model. Furthermore, we develop an improved empirical variant, Group Bias Adaptation (GBA), and \highlight{demonstrate its superior performance against benchmark methods when applied to LLMs with up to 1.5 billion parameters}. This work represents a foundational step in demystifying SAE training by providing the first SAE algorithm with theoretical recovery guarantees, thereby advancing the development of more transparent and trustworthy AI systems through enhanced mechanistic interpretability.