Vera Schmitt

A three-tier evaluation framework for unsupervised narrative label generation is introduced: recovery (against a corpus's own taxonomy), mining (against external label sets), and discovery (without predefined labels).

Automated benchmarks for explainability are fundamentally brittle: LLM "simulators" consistently game the metric by solving the task directly through semantic priors or exploiting label leakage rather than actually relying on the explanations.

Steering isn't just a trick; it's a fundamentally different way to adapt language models, offering localized, reversible control that traditional fine-tuning can't match.