Summary of Information Flow Routes: Automatically Interpreting Language Models at Scale, by Javier Ferrando and Elena Voita
Information Flow Routes: Automatically Interpreting Language Models at Scale
by Javier Ferrando, Elena Voita
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method automatically constructs graphs representing information flows within a neural network model. This is achieved through attribution, allowing for efficient extraction of circuits with a single forward pass. Unlike existing workflows relying on activation patching, this approach does not require human-designed prediction templates and can be applied to any prediction type or domain. The study demonstrates the applicability of this method using Llama 2, highlighting the importance of certain attention heads and similarities in handling tokens of the same part of speech. Additionally, it shows that model components can be specialized for domains like coding or multilingual texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how information flows within a neural network model. It’s like trying to understand how a complex machine works! The researchers found a way to automatically build maps showing where the information goes through the model. This is helpful because it lets us see what parts of the model are important and why. They tested this on Llama 2, a popular language model, and discovered that some parts of the model are more important than others. For example, certain attention heads play a crucial role in understanding text. The study also shows that different domains, like coding or multilingual texts, can have their own special features. |
Keywords
» Artificial intelligence » Attention » Language model » Llama » Neural network
