Summary of Persistent Topological Features in Large Language Models, by Yuri Gardinazzi and Giada Panerai and Karthik Viswanathan and Alessio Ansuini and Alberto Cazzaniga and Matteo Biagetti
Persistent Topological Features in Large Language Models
by Yuri Gardinazzi, Giada Panerai, Karthik Viswanathan, Alessio Ansuini, Alberto Cazzaniga, Matteo Biagetti
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computational Geometry (cs.CG); 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 A novel framework based on zigzag persistence from topological data analysis (TDA) is presented to characterize the internal representations of large language models (LLMs). The framework introduces persistence similarity, a new metric that captures the evolution of topological features throughout model layers, providing deeper insights into LLM decision-making processes. This approach is used to identify and prune redundant layers, achieving comparable performance to state-of-the-art methods on several benchmark datasets. Additionally, consistent topological behaviors are observed across various models and hyperparameter settings, suggesting a universal structure in LLM internal representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have many applications, but we don’t fully understand how they make decisions. To help with this, some researchers have looked at the shapes of information inside LLMs. This paper takes it to the next level by using a special math tool called zigzag persistence to study these internal representations. The authors come up with a new way to measure how much these shapes change as the model works on different tasks. They use this measurement, called persistence similarity, to find and remove unimportant parts of the model. This helps the model work just as well but uses fewer resources. The researchers also found that LLMs from different models and settings all share some common patterns in how they process information. |
Keywords
» Artificial intelligence » Hyperparameter
