Summary of Uncertainty Quantification in Large Language Models Through Convex Hull Analysis, by Ferhat Ozgur Catak and Murat Kuzlu
Uncertainty Quantification in Large Language Models Through Convex Hull Analysis
by Ferhat Ozgur Catak, Murat Kuzlu
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis in large language models (LLMs). Traditional methods face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. The proposed method leverages spatial properties of response embeddings to measure dispersion and variability of model outputs. The approach uses Principal Component Analysis (PCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster embeddings and compute convex hulls. The results show that uncertainty depends on prompt complexity, LLM model, and temperature setting. This work is crucial for reliable outputs in high-risk applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models like us can get very confused when trying to figure out how certain they are about their answers! This study helps solve this problem by coming up with a new way to measure uncertainty using special math tricks. They took lots of answers from different models and put them into a special space where we can see patterns. Then, they grouped the answers together based on what’s similar and what’s not. The results show that how certain the model is depends on how easy or hard the question is, which model is used, and how “hot” the model is (like how much coffee it’s had!). This helps us make more accurate predictions in important situations. |
Keywords
» Artificial intelligence » Clustering » Pca » Principal component analysis » Prompt » Temperature
