Mapping Brains with Language Models: A Survey

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Mapping Brains with Language Models : A Survey. / Karamolegkou, Antonia; Abdou, Mostafa; Søgaard, Anders.

Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics (ACL), 2023. p. 9748-9762.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Karamolegkou, A, Abdou, M & Søgaard, A 2023, Mapping Brains with Language Models: A Survey. in Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics (ACL), pp. 9748-9762, Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, 01/07/2023. https://doi.org/10.18653/v1/2023.findings-acl.618

APA

Karamolegkou, A., Abdou, M., & Søgaard, A. (2023). Mapping Brains with Language Models: A Survey. In Findings of the Association for Computational Linguistics: ACL 2023 (pp. 9748-9762). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-acl.618

Vancouver

Karamolegkou A, Abdou M, Søgaard A. Mapping Brains with Language Models: A Survey. In Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics (ACL). 2023. p. 9748-9762 https://doi.org/10.18653/v1/2023.findings-acl.618

Author

Karamolegkou, Antonia ; Abdou, Mostafa ; Søgaard, Anders. / Mapping Brains with Language Models : A Survey. Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics (ACL), 2023. pp. 9748-9762

Bibtex

@inproceedings{2248b102197844fdb3419a567fffeca9,
title = "Mapping Brains with Language Models: A Survey",
abstract = "Over the years, many researchers have seemingly made the same observation: Brain and language model activations exhibit some structural similarities, enabling linear partial mappings between features extracted from neural recordings and computational language models. In an attempt to evaluate how much evidence has been accumulated for this observation, we survey over 30 studies spanning 10 datasets and 8 metrics. How much evidence has been accumulated, and what, if anything, is missing before we can draw conclusions? Our analysis of the evaluation methods used in the literature reveals that some of the metrics are less conservative. We also find that the accumulated evidence, for now, remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism.",
author = "Antonia Karamolegkou and Mostafa Abdou and Anders S{\o}gaard",
year = "2023",
doi = "10.18653/v1/2023.findings-acl.618",
language = "English",
pages = "9748--9762",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
publisher = "Association for Computational Linguistics (ACL)",
address = "United States",
note = "Findings of the Association for Computational Linguistics: ACL 2023 ; Conference date: 01-07-2023 Through 01-07-2023",

}

RIS

TY - GEN

T1 - Mapping Brains with Language Models

T2 - Findings of the Association for Computational Linguistics: ACL 2023

AU - Karamolegkou, Antonia

AU - Abdou, Mostafa

AU - Søgaard, Anders

PY - 2023

Y1 - 2023

N2 - Over the years, many researchers have seemingly made the same observation: Brain and language model activations exhibit some structural similarities, enabling linear partial mappings between features extracted from neural recordings and computational language models. In an attempt to evaluate how much evidence has been accumulated for this observation, we survey over 30 studies spanning 10 datasets and 8 metrics. How much evidence has been accumulated, and what, if anything, is missing before we can draw conclusions? Our analysis of the evaluation methods used in the literature reveals that some of the metrics are less conservative. We also find that the accumulated evidence, for now, remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism.

AB - Over the years, many researchers have seemingly made the same observation: Brain and language model activations exhibit some structural similarities, enabling linear partial mappings between features extracted from neural recordings and computational language models. In an attempt to evaluate how much evidence has been accumulated for this observation, we survey over 30 studies spanning 10 datasets and 8 metrics. How much evidence has been accumulated, and what, if anything, is missing before we can draw conclusions? Our analysis of the evaluation methods used in the literature reveals that some of the metrics are less conservative. We also find that the accumulated evidence, for now, remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism.

U2 - 10.18653/v1/2023.findings-acl.618

DO - 10.18653/v1/2023.findings-acl.618

M3 - Article in proceedings

SP - 9748

EP - 9762

BT - Findings of the Association for Computational Linguistics: ACL 2023

PB - Association for Computational Linguistics (ACL)

Y2 - 1 July 2023 through 1 July 2023

ER -

ID: 381566615