Interventional Probing in High Dimensions: An NLI Case Study

Julia Rozanova, Marco Valentino, Lucas Cordeiro, André Freitas

Abstract
Probing strategies have been shown to detectthe presence of various linguistic features inlarge language models; in particular, seman-tic features intermediate to the “natural logic”fragment of the Natural Language Inferencetask (NLI). In the case of natural logic, the rela-tion between the intermediate features and theentailment label is explicitly known: as such,this provides a ripe setting for interventionalstudies on the NLI models’ representations, al-lowing for stronger causal conjectures and adeeper critical analysis of interventional prob-ing methods. In this work, we carry out newand existing representation-level interventionsto investigate the effect of these semantic fea-tures on NLI classification: we perform am-nesic probing (which removes features as di-rected by learned linear probes) and introducethe mnestic probing variation (which forgetsall dimensions except the probe-selected ones).Furthermore, we delve into the limitations ofthese methods and outline some pitfalls havebeen obscuring the effectivity of interventionalprobing studies.
Anthology ID:
2023.findings-eacl.188
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2489–2500
Language:
URL:
https://aclanthology.org/2023.findings-eacl.188
DOI:
10.18653/v1/2023.findings-eacl.188
Bibkey:
Cite (ACL):
Julia Rozanova, Marco Valentino, Lucas Cordeiro, and André Freitas. 2023. Interventional Probing in High Dimensions: An NLI Case Study. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2489–2500, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Interventional Probing in High Dimensions: An NLI Case Study (Rozanova et al., Findings 2023)
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