Summary of Distinguishing Chatbot From Human, by Gauri Anil Godghase and Rishit Agrawal and Tanush Obili and Mark Stamp
Distinguishing Chatbot from Human
by Gauri Anil Godghase, Rishit Agrawal, Tanush Obili, Mark Stamp
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 addresses the challenge of distinguishing between human-written and machine-generated text in the era of Large Language Models (LLM) and Generative Pre-trained Transformer (GPT)-based chatbots. To tackle this issue, a new dataset was created, comprising over 750,000 human-written paragraphs alongside corresponding chatbot-generated texts. The authors applied Machine Learning techniques to determine the origin of the text using two methodologies: feature analysis and embeddings. Specifically, they explored extracting features from text for classification and utilized contextual embeddings and transformer-based architectures to train classification models. The proposed solutions demonstrated high classification accuracy, serving as valuable tools for textual analysis and enhancing our understanding of chatbot-generated text in the context of advanced AI technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where computers can write text that’s almost indistinguishable from human-written work. This is already happening with advanced language models like GPT. To understand how to tell the difference between human and computer-written text, researchers created a massive dataset of over 750,000 paragraphs written by humans and their computer-generated counterparts. They then used special algorithms to analyze these texts and figure out which was which. Their approach showed that it’s possible to accurately identify whether text is human or machine-written, which could have important implications for fields like language analysis and AI development. |
Keywords
» Artificial intelligence » Classification » Gpt » Machine learning » Transformer
