Understanding the inner workings of large language models (LLMs) has always been a challenge in the field of artificial intelligence. The latest research result of Google DeepMind, Gemma Scope, provides a new method to explore the internal mechanism of LLM. It helps researchers better understand the decision-making process of the model by analyzing model activation vectors, thereby improving the interpretability and reliability of the model. Gemma Scope is not a simple visualization tool, but a carefully trained system that can perform in-depth analysis of different components of the model and quantify their impact.
Gemma Scope has been carefully trained on the activation of the Gemma2 model. During the training process, the activation vectors of the model are normalized, and SAEs are trained at different layers and locations, including attention head output, MLP output, and post-MLP residual flow.
Gemma Scope's performance was evaluated from multiple perspectives. Experimental results show that the delta loss of residual flow SAEs is usually higher, while sequence length has a significant impact on SAE performance. In addition, the performance of different dataset subsets is also different, and Gemma Scope performs best on DeepMind mathematics.
The release of Gemma Scope provides the possibility to solve a series of open problems. Not only can it help us understand SAEs more deeply, it can also improve the performance of real tasks and even red team test SAEs to determine whether they actually find the "real" concepts in the model.
With the application of Gemma Scope, we are expected to take a major step forward in the explainability and security of AI. It will help us better understand the inner workings of language models and improve the transparency and reliability of the models.
Paper address: https://storage.googleapis.com/gemma-scope/gemma-scope-report.pdf
Online experience: https://www.neuronpedia.org/gemma-scope#main
All in all, Gemma Scope provides a valuable tool for understanding large language models. It not only helps researchers deeply explore the internal mechanisms of the model, but also paves the way to improve the interpretability and security of AI. It has broad application prospects in the future. We look forward to Gemma Scope playing a greater role in the field of artificial intelligence.