@inproceedings{chanin-etal-2024-identifying,
title = "Identifying Linear Relational Concepts in Large Language Models",
author = "Chanin, David and
Hunter, Anthony and
Camburu, Oana-Maria",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.85",
pages = "1524--1535",
abstract = "Transformer language models (LMs) have been shown to represent concepts as directions in the latent space of hidden activations. However, for any human-interpretable concept, how can we find its direction in the latent space? We present a technique called linear relational concepts (LRC) for finding concept directions corresponding to human-interpretable concepts by first modeling the relation between subject and object as a linear relational embedding (LRE). We find that inverting the LRE and using earlier object layers results in a powerful technique for finding concept directions that outperforms standard black-box probing classifiers. We evaluate LRCs on their performance as concept classifiers as well as their ability to causally change model output.",
}
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%0 Conference Proceedings
%T Identifying Linear Relational Concepts in Large Language Models
%A Chanin, David
%A Hunter, Anthony
%A Camburu, Oana-Maria
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F chanin-etal-2024-identifying
%X Transformer language models (LMs) have been shown to represent concepts as directions in the latent space of hidden activations. However, for any human-interpretable concept, how can we find its direction in the latent space? We present a technique called linear relational concepts (LRC) for finding concept directions corresponding to human-interpretable concepts by first modeling the relation between subject and object as a linear relational embedding (LRE). We find that inverting the LRE and using earlier object layers results in a powerful technique for finding concept directions that outperforms standard black-box probing classifiers. We evaluate LRCs on their performance as concept classifiers as well as their ability to causally change model output.
%U https://aclanthology.org/2024.naacl-long.85
%P 1524-1535
Markdown (Informal)
[Identifying Linear Relational Concepts in Large Language Models](https://aclanthology.org/2024.naacl-long.85) (Chanin et al., NAACL 2024)
ACL
- David Chanin, Anthony Hunter, and Oana-Maria Camburu. 2024. Identifying Linear Relational Concepts in Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1524–1535, Mexico City, Mexico. Association for Computational Linguistics.