Summary of Few-shot Detection Of Machine-generated Text Using Style Representations, by Rafael Rivera Soto et al.
Few-Shot Detection of Machine-Generated Text using Style Representations
by Rafael Rivera Soto, Kailin Koch, Aleem Khan, Barry Chen, Marcus Bishop, Nicholas Andrews
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper proposes a novel approach to detecting whether a piece of text was written by a human or a language model. The authors aim to address the limitations of previous methods, which rely on supervised training with corpora of confirmed human-and machine-written documents. They introduce a fundamentally different approach that leverages representations of writing style estimated from human-authored text. This method is effective in distinguishing between human and machine authors, including state-of-the-art language models like Llama-2, ChatGPT, and GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out whether someone wrote a piece of text by themselves or used a computer program to help them write it. This is important because some people might use these programs to trick others into believing something that’s not true. The problem with previous methods is that they need lots of examples of texts written by humans and computers, which isn’t always possible. So, the authors came up with a new way to do this using just a few examples of what each computer program can write. This helps us know for sure whether something was written by a human or a computer. |
Keywords
* Artificial intelligence * Gpt * Language model * Llama * Supervised
