Meta-Models: An Architecture for Decoding LLM Behaviors Through Interpreted Embeddings and Natural Language
<|reference_start|>Meta-Models: An Architecture for Decoding LLM Behaviors Through Interpreted Embeddings and Natural Language: 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.<|reference_end|>
@article{costarelli2024meta-models:,
    title={Meta-Models: An Architecture for Decoding LLM Behaviors Through
  Interpreted Embeddings and Natural Language},
    author={Anthony Costarelli, Mat Allen, Severin Field},
    journal={arXiv preprint arXiv:2410.02472},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02472},
    primaryClass={cs.LG cs.AI}
}