Interventional Probing in High Dimensions: An NLI Case Study

Julia Rozanova, Marco Valentino, Lucas Cordeiro, Andre Freitas

Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionarypeer-review


Probing strategies have been shown to detect the presence of various linguistic features in large language models; in particular, semantic features intermediate to the “natural logic" fragment of the Natural Language Inference task (NLI). In the case of natural logic, the relation between the intermediate features and the entailment label is explicitly known: as such, this provides a ripe setting for interventional studies on the NLI models’ representations, allowing for stronger causal conjectures and a deeper critical analysis of interventional probing methods. In this work, we carry out new and existing representation-level interventions to investigate the effect of these semantic features on NLI classification: we perform amnesic probing (which removes features as directed by learned linear probes) and introduce the mnestic probing variation (which forgets all dimensions except the probe-selected ones). Furthermore, we delve into the limitations of these methods and outline some pitfalls have been obscuring the effectivity of interventional probing studies.
Original languageEnglish
Title of host publicationFindings of the European chapter of Association for Computational Linguistics (Findings of EACL), 2023.
Publication statusPublished - 2 May 2023


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