Researchers at Google have developed Gemma Scope, a novel tool designed to provide a deeper understanding of how individual layers in large language models (LLMs) process information. This tool is specifically tailored for the Gemma 2 family of LLMs.
Key Contributions:
- Sparse Autoencoder (SAE) Application: The research leverages SAEs to reconstruct input embeddings, enabling the identification of distinct concepts within each layer.
- Interpretable Embeddings: By analyzing the weights in the SAE’s first layer, researchers can determine the relative importance of various concepts in the input.
- Manual and Automatic Labeling: Gemma Scope incorporates both manual and automatic methods for labeling SAE indices, providing flexibility in the interpretation process.
- Steering LLM Behavior: The tool allows researchers to manipulate the model’s output by adjusting the values of specific indices in the SAE’s first layer.
Significance:
Gemma Scope represents a significant advancement in understanding the internal workings of LLMs. By providing a granular view of layer-level behavior, this tool can help researchers address fundamental questions about how these models process information and make decisions. This knowledge can ultimately lead to the development of more effective and interpretable LLMs.
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Source: https://ai.google.dev/gemma/docs/gemma_scope
https://www.deeplearning.ai/the-batch/googles-gemma-scope-probes-how-large-language-models-think
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