lex-ec-1b24b722·1 events·first seen Aliases: LEX-EC
Researchers introduce LEX-EC, a black-box audit framework for evaluating how large language models assign personality labels from text. The framework combines prevalence and agreement diagnostics with controlled lexical ablation to distinguish genuine trait-associated signal from marginal-distribution artifacts. Applied across text genres (essays, graduate introductions, Facebook statuses), the framework reveals that signal strength varies substantially by content type and that linguistic prompting shifts model self-explanations without eliminating topical content. The work contributes a reusable methodology for interpretability auditing of personality classification in LLMs.