GPT-4 Surpasses GPT-3.5 in Beating AI Plagiarism Detection

Table of Contents

• Comparing GPT-3.5 and GPT-4 Article Originality
• Optimizing GPT-4 to Outsmart Plagiarism Checkers
• Key Takeaways and Conclusion

Comparing GPT-3.5 and GPT-4 Article Originality

In this blog post, we will be comparing the originality and plagiarism scores of articles generated by GPT-3.5 and GPT-4. I ran an experiment generating 3 articles on dog-related topics using both models, then analyzed the outputs using AI content scoring and plagiarism detection tools.

The goal was to evaluate how creative and original GPT-4 is compared to GPT-3.5, and determine the best settings to optimize GPT-4 to avoid plagiarism detection.

Read on for the full experiment results and findings on properly tuning GPT-4 parameters like temperature, frequency penalty, and presence penalty to maximize originality.

Test Setup and Keywords

I generated 3 articles each for GPT-3.5 and GPT-4 on ChatGPT, using the same keywords for both models. The topics were: can dogs eat bananas, can dogs eat ramen, and can dogs eat pizza. These generic dog-focused keywords allowed me to directly compare originality across both models. I enabled titles in GPT-4 outputs and left titles off for GPT-3.5.

GPT-3.5 vs GPT-4 Originality Scores

I analyzed all 6 outputs using an AI content scoring tool. For the banana article, GPT-3.5 scored 100% AI while GPT-4 scored 97% original. For ramen, GPT-3.5 scored 99% AI and GPT-4 95% original. Finally, for pizza, GPT-3.5 was 99% AI and GPT-4 was 76% original. Clearly, GPT-4 demonstrates significantly higher originality than GPT-3.5. But there is still room for improvement, as a 76% originality score indicates some repetitiveness.

Checking for Plagiarism

I also checked the articles for plagiarism using Grammarly. GPT-3.5 banana scored 22% plagiarism, ramen 14%, and pizza 14%. GPT-4 scored 12%, 3%, and 3% respectively. Again, GPT-4 outputs had lower plagiarism versus GPT-3.5 across all tests. But the model can still be further optimized to minimize plagiarism scores.

Optimizing GPT-4 to Outsmart Plagiarism Checkers

Given that GPT-4 plagiarism scores, while better than GPT-3.5, indicate some repetitive content, I wanted to tune the model to maximize originality and avoid detection.

GPT-4 has temperature, frequency penalty, and presence penalty parameters that impact randomness, word patterns, and repetition. I experimented to find the best settings.

Temperature, Frequency and Presence Penalties

Higher temperature increases randomness and creativity. However, I found that maximizing temperature does not necessarily mean better plagiarism scores. Surprisingly, a temperature of 0.5 worked best. Higher frequency and presence penalties minimize repetition of common words and patterns. Settings of 0.5 for both parameters worked optimally.

Best GPT-4 Settings to Avoid Detection

Based on my testing, the best settings to reduce plagiarism and AI detection are:

• Temperature: 0.5
• Frequency Penalty: 0.5
• Presence Penalty: 0.5 With these parameters, GPT-4 returned a great quality original article scoring 100% human with almost no plagiarism.

Priming GPT-3.5, But Limited Success

I tried "priming" GPT-3.5 with instructions to use the optimized settings. It seemed to understand but failed to implement them properly, scoring just 3% originality. This demonstrates GPT-4's superior comprehension and ability to dynamically adjust output parameters compared to GPT-3.5.

Key Takeaways and Conclusion

My testing revealed that GPT-4 produces markedly more original content versus GPT-3.5. It has significantly higher originality scores and lower plagiarism.

With optimized temperature, frequency penalty, and presence penalty settings, GPT-4 can generate highly original articles avoiding plagiarism detection.

However, GPT-3.5 fails to properly implement parameter instructions, indicating inferior comprehension. GPT-4 represents a major leap in originality and comprehension capabilities.

FAQ

Q: How was GPT-3.5 vs GPT-4 originality tested?
A: 3 articles were generated in GPT-3.5 and GPT-4 on the same keywords - can dogs eat bananas, lettuce and pizza. Originality was checked through AI detectors.

Q: What were the GPT-3.5 and GPT-4 originality scores?
A: GPT-3.5 scored 99-100% AI generated. GPT-4 scored 76-97% original human writing.

Q: How does GPT-4 outperform GPT-3.5 in beating plagiarism checkers?
A: By optimizing temperature, frequency and presence penalties, GPT-4 produces more original content less detectable by AI.

Q: What are the best GPT-4 settings to avoid AI detection?
A: Temperature 0.5, frequency penalty 0.5, presence penalty 0.5 works well by lowering randomness and repetition.

Q: Can GPT-3.5 also be optimized to beat plagiarism checkers?
A: Priming helps but is limited. GPT-3.5 still scored only 3% originality compared to GPT-4's 90%.

Q: What is the key takeaway from comparing GPT-3.5 and GPT-4?
A: GPT-4 is superior in creating more human-like, original content that can bypass plagiarism detectors.

Q: Where can I access the full GPT-3.5 vs GPT-4 test outputs?
A: The original outputs are available in the blog post called "GPT-4 Pass AI Detection" at trickmenot.ai.

Q: Can these GPT-4 settings be used elsewhere to beat plagiarism detection?
A: Yes, optimizing for lower randomness, less repetition tends to improve originality scores across the board.

Q: Does higher GPT-4 temperature always mean more AI detectability?
A: No, higher temperature does not directly correlate with lower originality. Optimizing all three penalties is key.

Q: Could GPT-3.5 match GPT-4's performance in beating plagiarism checkers?
A: Unlikely based on tests. GPT-3.5 struggled to incorporate optimized settings without errors.