Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers
Published:Dec 17, 2025 18:26
•1 min read
•ArXiv
Analysis
This article, sourced from ArXiv, focuses on the development and evaluation of Large Language Models (LLMs) designed to explain the internal activations of other LLMs. The core idea revolves around training LLMs to act as 'activation explainers,' providing insights into the decision-making processes within other models. The research likely explores methods for training these explainers, evaluating their accuracy and interpretability, and potentially identifying limitations or biases in the explained models. The use of 'oracles' suggests a focus on providing ground truth or reliable explanations for comparison and evaluation.
Key Takeaways
- •Focuses on using LLMs to explain the internal workings of other LLMs.
- •Employs the concept of 'activation explainers' to provide insights into model decision-making.
- •Likely explores training, evaluation, and potential limitations of these explainers.
- •The use of 'oracles' suggests a focus on ground truth explanations for comparison.
Reference
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