Summary of Online Detecting Llm-generated Texts Via Sequential Hypothesis Testing by Betting, By Can Chen et al.
Online Detecting LLM-Generated Texts via Sequential Hypothesis Testing by Betting
by Can Chen, Jun-Kun Wang
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 A newly developed algorithm for distinguishing between machine-generated texts and human-written texts is presented in this paper. The existing methods are mainly focused on offline settings, where a dataset containing both real and machine-generated texts is provided. However, in many practical scenarios, content is published online in a streaming fashion, making it crucial to develop an effective method for detecting whether the source is a large language model (LLM) or a human with strong statistical guarantees. The proposed algorithm uses sequential hypothesis testing by betting, which not only builds upon existing offline detection techniques but also enjoys controlled false positive rates and expected times to correctly identify sources as LLMS. Experimental results demonstrate the effectiveness of this method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to tell apart machine-made writing from human-written text. This happens in a special setting where text comes out one piece at a time, like on news websites or social media. Right now, most methods work when you have a big collection of texts beforehand. But we need something that works well online so that people can trust what they read and not spread false information. To solve this problem, an algorithm is created using special math techniques. This method is good at finding out if the writer is a computer or a person, with some guarantees about how often it makes mistakes. The results show that this new way works well. |
Keywords
» Artificial intelligence » Large language model
