Summary of Estimating the Hallucination Rate Of Generative Ai, by Andrew Jesson and Nicolas Beltran-velez and Quentin Chu and Sweta Karlekar and Jannik Kossen and Yarin Gal and John P. Cunningham and David Blei
Estimating the Hallucination Rate of Generative AI
by Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David Blei
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposes a method to estimate the hallucination rate in context learning (ICL) with generative AI, which involves prompting a conditional generative model (CGM) to generate responses. The authors define hallucinations as generated responses with low likelihood given the mechanism. They develop an estimation method that only requires generating prediction questions and responses from the CGM and evaluating its response log probability. This method is evaluated using large language models for synthetic regression and natural language ICL tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a way to measure how often generative AI makes mistakes when trying to answer questions based on what it has learned. The authors believe that this is important because AI might not always understand the question or the task, even if it looks like it does. They came up with a new method to figure out how likely an AI model is to make these kinds of mistakes. |
Keywords
» Artificial intelligence » Generative model » Hallucination » Likelihood » Probability » Prompting » Regression
