Summary of Technical Report on the Pangram Ai-generated Text Classifier, by Bradley Emi and Max Spero
Technical Report on the Pangram AI-Generated Text Classifier
by Bradley Emi, Max Spero
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Pangram Text is a transformer-based neural network designed to identify text written by large language models versus human-written text. The model outperforms zero-shot methods and commercial AI detection tools, achieving over 38 times lower error rates on a comprehensive benchmark of 10 text domains and 8 open- and closed-source language models. Pangram Text’s training algorithm, hard negative mining with synthetic mirrors, enables it to achieve significantly lower false positive rates on high-data domains like reviews. Additionally, the model is not biased against nonnative English speakers and generalizes to unseen domains and models during training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pangram Text is a special computer program that can tell if text was written by a machine or a person. This program is really good at getting it right! It even beats other programs that don’t need any training. Pangram Text looks at many different types of texts, like student essays and news articles, to figure out what makes human-written text unique. The program also does well on texts from people who aren’t native English speakers. |
Keywords
» Artificial intelligence » Neural network » Transformer » Zero shot
