@inproceedings{lee-etal-2026-survey,
title = "A Survey on the Linear Representation Hypothesis",
author = "Lee, Sewoong and
Canby, Marc E. and
Cho, Ikhyun and
Hockenmaier, Julia",
editor = "Arad, Dana and
Belinkov, Yonatan and
Chen, Hanjie and
Feldhus, Nils and
Mueller, Aaron and
Mohebbi, Hosein and
Sarti, Gabriele and
Tutek, Martin",
booktitle = "Proceedings of the 9th {B}lackbox{NLP} Workshop: Analyzing and Interpreting Neural Networks for {NLP}",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.blackboxnlp-1.48/",
pages = "835--851",
ISBN = "979-8-89176-476-7",
abstract = "The term ``linear representation hypothesis'' (LRH) has appeared across diverse subfields of artificial intelligence, neuroscience, and cognitive science. But previous works have not consistently treated the LRH as a falsifiable scientific hypothesis; we analyze these inconsistencies and examine their implications for how prior theoretical and methodological results should be interpreted. Based on this analysis, we argue that claims regarding linear representations become well-defined only through careful examination of the model, representation location, feature definition, and evaluation dataset. We therefore propose a more rigorous formalization of the LRH that makes these dependencies explicit and allows the hypothesis to be evaluated as a falsifiable scientific claim. Finally, we identify some non-trivial open problems that warrant further attention from the research community."
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%0 Conference Proceedings
%T A Survey on the Linear Representation Hypothesis
%A Lee, Sewoong
%A Canby, Marc E.
%A Cho, Ikhyun
%A Hockenmaier, Julia
%Y Arad, Dana
%Y Belinkov, Yonatan
%Y Chen, Hanjie
%Y Feldhus, Nils
%Y Mueller, Aaron
%Y Mohebbi, Hosein
%Y Sarti, Gabriele
%Y Tutek, Martin
%S Proceedings of the 9th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-476-7
%F lee-etal-2026-survey
%X The term “linear representation hypothesis” (LRH) has appeared across diverse subfields of artificial intelligence, neuroscience, and cognitive science. But previous works have not consistently treated the LRH as a falsifiable scientific hypothesis; we analyze these inconsistencies and examine their implications for how prior theoretical and methodological results should be interpreted. Based on this analysis, we argue that claims regarding linear representations become well-defined only through careful examination of the model, representation location, feature definition, and evaluation dataset. We therefore propose a more rigorous formalization of the LRH that makes these dependencies explicit and allows the hypothesis to be evaluated as a falsifiable scientific claim. Finally, we identify some non-trivial open problems that warrant further attention from the research community.
%U https://aclanthology.org/2026.blackboxnlp-1.48/
%P 835-851
Markdown (Informal)
[A Survey on the Linear Representation Hypothesis](https://aclanthology.org/2026.blackboxnlp-1.48/) (Lee et al., BlackboxNLP 2026)
ACL
- Sewoong Lee, Marc E. Canby, Ikhyun Cho, and Julia Hockenmaier. 2026. A Survey on the Linear Representation Hypothesis. In Proceedings of the 9th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 835–851, Budapest, Hungary. Association for Computational Linguistics.