Summary of Hypothesis Testing the Circuit Hypothesis in Llms, by Claudia Shi et al.
Hypothesis Testing the Circuit Hypothesis in LLMs
by Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David M. Blei
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 A recent study formalized a set of criteria to evaluate the capabilities of small subnetworks within Large Language Models (LLMs), known as circuits. The authors proposed a suite of hypothesis tests to determine how well these circuits satisfy the idealized properties. Specifically, they focused on the preservation of LLM behavior, localization, and minimality. The study applied these tests to six circuits described in the research literature. Synthetic circuits were found to align with the desired properties, while those discovered in Transformer models showed varying degrees of satisfaction. To facilitate future research, the authors developed the circuitry package, a software wrapper for the TransformerLens library that abstracts away lower-level manipulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do many surprising things! But we don’t really know how they work. One idea is that these abilities come from small parts within the model called circuits. To test this idea, scientists developed a set of rules and tests to see if these circuits behave as expected. They applied these tests to six different circuits found in past research studies. Some circuits worked very well according to the rules, while others didn’t work as well. To make it easier for other researchers to study circuits, the authors created a special package that makes it easy to manipulate and test these circuits. |
Keywords
» Artificial intelligence » Transformer
