Summary of Unveiling Language Skills Via Path-level Circuit Discovery, by Hang Chen and Jiaying Zhu and Xinyu Yang and Wenya Wang
Unveiling Language Skills via Path-Level Circuit Discovery
by Hang Chen, Jiaying Zhu, Xinyu Yang, Wenya Wang
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 path-level circuit discovery framework captures complex behaviors emerging through interconnected linear chain and builds towards mechanism interpretability of language models. By decomposing the original model into fully-disentangled memory circuits, the framework leverages a 2-step pruning strategy to identify common paths of specific skills, such as Previous Token Skill, Induction Skill, and In-Context Learning Skill. This approach contrasts with circuit graph methods that focus on fine-grained responses to individual components. The proposed framework provides more compelling evidence for stratification and inclusiveness of language skills. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to understand how language models work by looking at the paths they take when processing text. It proposes a framework that breaks down the model into simpler pieces, called “memory circuits,” and then finds the most important paths that help with specific tasks like understanding previous words or learning from context. The approach is different from others that focus on individual parts of the input. This work can help us better understand how language models make decisions. |
Keywords
» Artificial intelligence » Pruning » Token
