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arXiv · 2311.08968

Identifying Linear Relational Concepts in Large Language Models

David Chanin·Anthony Hunter·Oana-Maria Camburu
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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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David Chanin, Anthony Hunter, Oana-Maria Camburu. 2023-11-15. Identifying Linear Relational Concepts in Large Language Models. https://arxiv.org/abs/2311.08968

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