Summary of Differentiating Between Human-written and Ai-generated Texts Using Linguistic Features Automatically Extracted From An Online Computational Tool, by Georgios P. Georgiou
Differentiating between human-written and AI-generated texts using linguistic features automatically extracted from an online computational tool
by Georgios P. Georgiou
First submitted to arxiv on: 4 Jul 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 study investigates how ChatGPT’s generated texts compare to human-written ones in terms of linguistic features. Researchers prompted ChatGPT to generate essays similar in length and analyzed them using Open Brain AI, an online tool, to extract measures of phonological, morphological, syntactic, and lexical constituents. The results showed significant differences between the two types of texts across various linguistic features, such as consonants, word stress, nouns, verbs, pronouns, direct objects, prepositional modifiers, and use of difficult words. These findings highlight the importance of integrating automated tools for efficient language assessment, reducing time and effort in data analysis. Moreover, they emphasize the necessity for enhanced training methodologies to improve ChatGPT’s capacity for producing more human-like text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well AI-generated texts compare to ones written by humans. Researchers used a tool called Open Brain AI to analyze two types of essays: ones written by humans and ones generated by ChatGPT. The results showed that the AI-generated texts were different from human-written ones in many ways, like the use of certain words or phrases. This is important because it means we need better tools to help us understand language more quickly and easily. It also shows that we need to improve how we train AI systems so they can write more naturally like humans. |
