the practical value here is for regulated domains. in healthcare and finance you often cant deploy a model at all unless you can explain why it made a specific decision. token-level attribution that traces back to training data sources could satisfy audit requirements that currently block LLM adoption entirely.

curious how the performance compares to a standard llama 8b on benchmarks - interpretability usually comes with a quality tax.

the quality tax framing might actually undersell the value in regulated domains. if a hospital system can't deploy without explainability, a model that scores 95% and can trace its reasoning beats one that scores 97% and can't. the baseline isn't 'interpretable model vs better model' -- it's 'interpretable model vs no model at all.'

in the "Performance" section of the post: https://www.guidelabs.ai/post/steerling-8b-base-model-releas..., the authors show the model lags behind llama 8b but worth noting that llama 8b trained on > 2x more computes (see the FLOPs axis)