  Despite the recent success of large, pretrained neural language models (LLMs)
on a variety of prompting tasks, these models can be alarmingly brittle to
small changes in inputs or application contexts. To better understand such
behavior and motivate the design of more robust LLMs, we provide a causal
formulation of linguistic competence in the context of LLMs and propose a
general framework to study and measure LLM competence. Our framework, CALM
(Competence-based Analysis of Language Models), establishes the first
quantitative measure of LLM competence, which we study by damaging models'
internal representations of various linguistic properties in the course of
performing various tasks using causal probing and evaluating models' alignment
under these interventions with a given causal model. We also develop a novel
approach for performing causal probing interventions using gradient-based
adversarial attacks, which can target a broader range of properties and
representations than existing techniques. We carry out a case study of CALM
using these interventions to analyze BERT and RoBERTa's competence across a
variety of lexical inference tasks, showing that the CALM framework and
competence metric can be valuable tools for explaining and predicting their
behavior across these tasks.
