Identifying Linear Relational Concepts in Large Language Models

• URL: http://arxiv.org/abs/2311.08968v2
• Date: Fri, 29 Mar 2024 22:14:30 GMT
• Title: Identifying Linear Relational Concepts in Large Language Models
• Authors: David Chanin, Anthony Hunter, Oana-Maria Camburu,
• Abstract summary: Transformer language models (LMs) have been shown to represent concepts as directions in the latent space of hidden activations. We present a technique called linear relational concepts (LRC) for finding concept directions corresponding to human-interpretable concepts.
• Score: 16.917379272022064
• License: http://creativecommons.org/licenses/by/4.0/
• 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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