Summary of Attribot: a Bag Of Tricks For Efficiently Approximating Leave-one-out Context Attribution, by Fengyuan Liu et al.
AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution
by Fengyuan Liu, Nikhil Kandpal, Colin Raffel
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces AttriBoT, a set of techniques for efficiently computing context attribution in large language models (LLMs). The leave-one-out (LOO) error is used to quantify each context span’s effect on the LLM’s generations. However, this approach can be computationally expensive. AttriBoT uses cached activations to avoid redundant operations, hierarchical attribution to reduce computation, and proxy models to emulate large target models. This results in a >300x speedup while maintaining faithfulness to the LOO error. The implementation of AttriBoT enables efficient LLM interpretability and encourages future development of efficient context attribution methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make language models better understand where they got their ideas from. Right now, it’s hard to figure out which parts of a conversation or text made the model generate certain words or sentences. The authors developed a way called AttriBoT that makes this process much faster and more accurate. They used special tricks like using old calculations again and breaking down big tasks into smaller ones to make it work. This will help people use language models in real-life applications where they need to understand how the model is thinking. |
