arXiv:2605.29358v1 Announce Type: new Abstract: We demonstrate that sparse autoencoders can extract interpretable features from Claude 3 Sonnet, a production-scale language model, addressing the open question of whether dictionary learning methods scale beyond small transformers. We trained sparse autoencoders with up to 34 million features on the model's middle layer residual stream, using scaling laws to guide hyperparameter selection. The resulting features are multilingual and multimodal (generalizing to images despite text-only training), respond to both concrete instances and abstract di
The paper provides concrete evidence that interpretability techniques, previously thought limited to smaller models, can scale to production-grade LLMs like Claude 3 Sonnet.
Understanding the internal workings of large language models is crucial for their ethical deployment, safety, and further advancement, especially for models used in critical applications.
This research suggests a viable path towards more transparent and steerable large AI models, potentially accelerating development in model debugging, safety, and explainability.
- · AI researchers
- · Anthropic
- · AI safety organizations
- · Developers of interpretability tools
- · Proponents of 'black box' AI development
Improved debugging and understanding of large language models lead to more robust and reliable AI systems.
Greater trust in AI systems encourages broader adoption in sensitive industries, expanding the AI market.
The ability to 'read' the internal states of models could accelerate the development of truly agentic and self-improving AI by providing insights into their reasoning processes.
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Read at arXiv cs.AI