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Where Does Linguistic Information Emerge in Neural Language Models? Measuring Gains and Contributions across Layers

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A8T27XUX5" target="_blank" >RIV/00216208:11320/22:8T27XUX5 - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.coling-1.413" target="_blank" >https://aclanthology.org/2022.coling-1.413</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Where Does Linguistic Information Emerge in Neural Language Models? Measuring Gains and Contributions across Layers

  • Original language description

    Probing studies have extensively explored where in neural language models linguistic information is located. The standard approach to interpreting the results of a probing classifier is to focus on the layers whose representations give the highest performance on the probing task. We propose an alternative method that asks where the task-relevant information emerges in the model. Our framework consists of a family of metrics that explicitly model local information gain relative to the previous layer and each layer's contribution to the model's overall performance. We apply the new metrics to two pairs of syntactic probing tasks with different degrees of complexity and find that the metrics confirm the expected ordering only for one of the pairs. Our local metrics show a massive dominance of the first layers, indicating that the features that contribute the most to our probing tasks are not as high-level as global metrics suggest.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 29th International Conference on Computational Linguistics

  • ISBN

  • ISSN

    2951-2093

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    4664-4676

  • Publisher name

    International Committee on Computational Linguistics

  • Place of publication

  • Event location

    Gyeongju, Republic of Korea

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article