Meta-Models: An Architecture for Decoding LLM Behaviors Through Interpreted Embeddings and Natural Language

• URL: http://arxiv.org/abs/2410.02472v3
• Date: Thu, 07 Nov 2024 18:30:38 GMT
• Title: Meta-Models: An Architecture for Decoding LLM Behaviors Through Interpreted Embeddings and Natural Language
• Authors: Anthony Costarelli, Mat Allen, Severin Field,
• Abstract summary: We use a "meta-model" that takes activations from an "input-model" and answers natural language questions about the input-model's behaviors. We evaluate the meta-model's ability to generalize by training them on selected task types and assessing their out-of-distribution performance in deceptive scenarios.
• Score: 0.0
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• Abstract: As Large Language Models (LLMs) become increasingly integrated into our daily lives, the potential harms from deceptive behavior underlie the need for faithfully interpreting their decision-making. While traditional probing methods have shown some effectiveness, they remain best for narrowly scoped tasks while more comprehensive explanations are still necessary. To this end, we investigate meta-models-an architecture using a "meta-model" that takes activations from an "input-model" and answers natural language questions about the input-model's behaviors. We evaluate the meta-model's ability to generalize by training them on selected task types and assessing their out-of-distribution performance in deceptive scenarios. Our findings show that meta-models generalize well to out-of-distribution tasks and point towards opportunities for future research in this area. Our code is available at https://github.com/acostarelli/meta-models-public .

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