Summary of Your Large Language Models Are Leaving Fingerprints, by Hope Mcgovern and Rickard Stureborg and Yoshi Suhara and Dimitris Alikaniotis
Your Large Language Models Are Leaving Fingerprints
by Hope McGovern, Rickard Stureborg, Yoshi Suhara, Dimitris Alikaniotis
First submitted to arxiv on: 22 May 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 This paper investigates the feasibility of detecting machine-generated text using various classification approaches and analyzes the unique patterns or “fingerprints” present in Large Language Model (LLM) output. The authors demonstrate that even simple classifiers can achieve high accuracy on both in-domain and out-of-domain data when leveraged with n-gram and part-of-speech features. They visualize these fingerprints, showcasing their persistence across models within the same family, such as llama-13b vs. llama-65b. Additionally, they find that LLMs fine-tuned for chat are more detectable than standard language models, suggesting that these fingerprints may be induced by training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computers can tell apart human-written text from machine-generated text. Researchers found that even simple computer programs can do this job very well if they use the right clues from the text. They analyzed five big datasets and discovered that these machines leave behind special patterns or “fingerprints” that make them easy to identify. These fingerprints stay the same across different machine models, making it easier to spot which texts were written by humans. |
Keywords
» Artificial intelligence » Classification » Large language model » Llama » N gram
