Abstract
Amnesic probing is a technique used to examine the influence of specific linguistic information on the behaviour of a model. This involves identifying and removing the relevant information and then assessing whether the model's performance on the main task changes. If the removed information is relevant, the model's performance should decline. The difficulty with this approach lies in removing only the target information while leaving other information unchanged. It has been shown that Iterative Nullspace Projection (INLP), a widely used removal technique, introduces random modifications to representations when eliminating target information. We demonstrate that Mean Projection (MP) and LEACE, two proposed alternatives, remove information in a more targeted manner, thereby enhancing the potential for obtaining behavioural explanations through Amnesic Probing.
| Original language | English |
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| Title of host publication | Findings of the Association for Computational Linguistics: ACL 2025 |
| Subtitle of host publication | [Proceedings] |
| Editors | Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 12981-12993 |
| Number of pages | 13 |
| ISBN (Electronic) | 9798891762565 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025 - Vienna, Austria Duration: 27 Jul 2025 → 1 Aug 2025 |
Publication series
| Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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| ISSN (Print) | 0736-587X |
Conference
| Conference | 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025 |
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| Country/Territory | Austria |
| City | Vienna |
| Period | 27/07/25 → 1/08/25 |
Bibliographical note
Publisher Copyright:© 2025 Association for Computational Linguistics.
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