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        <title>Interventional Probing in High Dimensions: An NLI Case Study</title>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.188</identifier>
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%0 Conference Proceedings
%T Interventional Probing in High Dimensions: An NLI Case Study
%A Rozanova, Julia
%A Valentino, Marco
%A Cordeiro, Lucas
%A Freitas, André
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F rozanova-etal-2023-interventional
%X 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.
%R 10.18653/v1/2023.findings-eacl.188
%U https://aclanthology.org/2023.findings-eacl.188/
%U https://doi.org/10.18653/v1/2023.findings-eacl.188
%P 2489-2500

Markdown (Informal)

[Interventional Probing in High Dimensions: An NLI Case Study](https://aclanthology.org/2023.findings-eacl.188/) (Rozanova et al., Findings 2023)

• Interventional Probing in High Dimensions: An NLI Case Study (Rozanova et al., Findings 2023)

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.