Summary of Knowledge Circuits in Pretrained Transformers, by Yunzhi Yao et al.
Knowledge Circuits in Pretrained Transformers
by Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, Huajun Chen
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 abstract discusses large language models’ ability to store knowledge within their parameters, enabling reasoning and perception. Researchers have investigated isolated components like Multilayer Perceptrons and attention heads, but this paper delves into the computation graph of a language model to uncover “knowledge circuits” that encode specific information. The study uses GPT2 and TinyLLAMA models to observe how different components collaborate in encoding knowledge. It also evaluates the impact of current editing techniques on these circuits and explores their potential for improving Transformer design and understanding language model behaviors like hallucinations and in-context learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper examines how large language models store information, which helps them reason and perceive the world. The researchers look at specific parts of these models to see how they work together to remember certain things. They use two types of models (GPT2 and TinyLLAMA) to study this process. The findings could help us better understand how these models learn and make mistakes, like creating fake information. |
Keywords
» Artificial intelligence » Attention » Language model » Transformer
