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.- Anthology ID:
- 2023.findings-acl.618
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9748–9762
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.618
- DOI:
- 10.18653/v1/2023.findings-acl.618
- Cite (ACL):
- Antonia Karamolegkou, Mostafa Abdou, and Anders Søgaard. 2023. Mapping Brains with Language Models: A Survey. In Findings of the Association for Computational Linguistics: ACL 2023, pages 9748–9762, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Mapping Brains with Language Models: A Survey (Karamolegkou et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.618.pdf