How Quantization Changes Interpretable Features: A Sparse Autoencoder Analysis of Language Models
arXiv:2606.03002v1 Announce Type: new Abstract: Quantization is a standard path to deploying large language models, and a quantized model is typically judged acceptable when its perplexity or downstream accuracy stays close to the full-precision original. Whether the model still computes in the same way, or whether the interpretable features identified in the full-precision model survive weight rounding, is rarely tested, even as safety audits and steering interventions increasingly rely on those features. We ask whether sparse autoencoder (SAE) features extracted from a dense full-precision m
The increasing reliance on quantized large language models for deployment necessitates understanding their internal workings beyond superficial performance metrics.
Ensuring the robustness of interpretable features in quantized models is critical for safety audits, steering interventions, and the overall trustworthiness of AI systems, moving beyond simple accuracy metrics.
The focus extends from merely achieving high perplexity or accuracy in quantized models to verifying that their interpretability and underlying computational mechanisms remain consistent with full-precision versions.
- · AI interpretability researchers
- · Model developers focused on safety and alignment
- · Quantization tool providers offering interpretability checks
- · Companies deploying quantized models without interpretability validation
- · Methodologies relying solely on perplexity/accuracy for quantization assessment
Further research and tooling will emerge to assess interpretable features in quantized models.
New standards and regulatory requirements might incorporate interpretability preservation as a key metric for AI deployment.
The development and adoption of AI systems in critical applications will be expedited due to increased explainability and trustworthiness.
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Read at arXiv cs.LG