A Comparative Analysis of GPT-3.5, GPT-4 Open Playground, Bart, and Bing

Ever since the release of OpenAI's GPT-3.5 model, there has been a lot of buzz in the AI community. Researchers and developers have been eager to compare this latest version with other existing models to evaluate its capabilities and performance. In this article, we will be comparing GPT-3.5 with GPT-4 Open Playground, Bart, and Bing, examining various aspects such as readability, SEO score, and originality.

Grounds for Comparison

Before delving into the comparisons, it is essential to establish the grounds for evaluation. To ensure a consistent approach, we will evaluate each model based on the following criteria:

1. Workout: This refers to the number of words generated by each model.
2. SEO Score: This measures how well the generated content aligns with search engine optimization practices.
3. Readability: The readability grade level of the content will be assessed using the Hemingway app.
4. Originality: We will analyze the originality of the content, measuring the uniqueness of the generated text.

Comparing GPT-3.5 and GPT-4 Open Playground

We will begin our comparison with GPT-3.5 and GPT-4 Open Playground. For both models, we have used the same prompts: "List entities and outside keywords for the seed keywords of 'candoxide login entities and side keywords'" and "Use the above information to write a 2000-word article using Markdown formatting with bulleted lists and tables, focusing on readability at an eighth-grade level."

GPT-3.5

The generated article by GPT-3.5 had a word count of approximately 687 words. It scored 59 in SEO measurement and had a reading grade level of eight, which aligns with the desired readability. However, the originality score was zero percent, indicating that the content lacks uniqueness.

GPT-4 Open Playground

Moving on to GPT-4 Open Playground, we observed a higher number of entities and LSI keywords in the generated content. The word count was 649 words, slightly lower than that of GPT-3.5. Surprisingly, the SEO score improved to 64, indicating better alignment with optimization practices. The reading grade level remained at eight. Just like GPT-3.5, the originality score of GPT-4 Open Playground was zero percent.

Analyzing GPT-4 Open Playground Further

To gain a deeper understanding of GPT-4 Open Playground's capabilities, we experimented with different settings and prompts. By adjusting the temperature, frequency penalty, and presence penalty, we were able to improve the originality score to 37 percent. However, this adjustment may have affected the visibility as the reading grade level decreased to nine. Overall, GPT-4 Open Playground showcased the potential to generate content that meets SEO requirements when properly tuned.

Exploring Bart and Bing

Next, we turn our attention to Bart and Bing, using the same prompts as before.

Bart

Bart generated a concise article with a word count of 464 words. While it did not provide content in Markdown formatting, it offered interesting elements such as tables and lists. The SEO score for Bart was 59, suggesting that it can be useful for extracting side keywords and entities for further analysis. However, the reading grade level was unexpectedly low at grade five, which may not be suitable for standard articles. Similar to the previous models, Bart also scored zero percent in originality.

Bing

Lastly, we used Bing's Creative mode to generate content based on the provided prompts. Bing showcased its capability to generate longer pieces with a word count of 617. The SEO score for the article was 65, slightly higher than the previous models. However, the reading grade level dropped to grade four, which may not be suitable for general readership. The originality score again stood at zero percent, aligning with the previous models.

Discovering the Potential of Text Generation Models

In our quest to explore the capabilities of text generation models, we tested various platforms such as Chegebd and Open AI Playground. While these platforms provided some interesting insights, we were not fully satisfied with the results.

However, amidst this experimentation, we stumbled upon an exciting feature. It turns out that certain models have the ability to generate responses based on keywords. This discovery has opened up new possibilities for us to delve deeper into the potential of these models.

Harnessing the Power of Keywords

One intriguing model that caught our attention is Hugging Chat. This model, based on Llama 2, impressed us with its ability to pass AI detection. In fact, it even surprised us with its recent addition of a web search function. With this feature enabled, the model can complement its answers with information sourced from the web.

We decided to put Hugging Chat to the test and were pleased with the results. By querying it with relevant keywords, we received a comprehensive list of entities related to our query. Although the list was not as extensive as other models, it still provided valuable insights.

Exploring Markdown Formatting

Another interesting aspect we explored was the use of Markdown formatting. This formatting method proved to be a useful tool in structuring our article. By utilizing lists and tables, we were able to present information in a more organized and visually appealing manner.

Speaking of tables, one of our articles yielded a surprising result - the inclusion of a table. This unexpected addition enhanced the overall look and feel of the article, showcasing the potential of Markdown formatting in generating aesthetically pleasing content.

Unveiling the Results

As we continued our experiments, we closely monitored the performance of each model. Evaluating factors such as SEO score, readability, and originality allowed us to gauge the quality of the generated text.

While the Chegebd and Open AI Playground models displayed decent SEO scores, their overall performance fell short in other areas. Hugging Chat, on the other hand, showed promise with its browsing feature, even though the other aspects were subpar. However, the real surprise came from Bing.

Bing took us by surprise with a well-formatted article that scored high on SEO and readability. The article featured almost 900 words that were beautifully presented and highly optimized for search engines. Its visibility and overall quality exceeded our expectations, proving that this free tool has immense potential.

Final Thoughts and Future Endeavors

Through this experimentation, we have gained valuable insights into the capabilities of different text generation models. While some models showed promise in certain aspects, it was Bing that stood out as the most reliable and impressive tool.

We hope you found this experiment helpful and intriguing. As we embrace the evolving landscape of text generation, we look forward to uncovering more hidden gems and sharing our findings with you. Like, share, and subscribe if you haven't already, and stay tuned for our future discoveries!In this comparative analysis, we explored the performance of gpt-3.5, gpt-4 open playground, bart, and bing in generating content based on given prompts. each model demonstrated its own strengths and weaknesses. while gpt-4 open playground showed potential in aligning with seo measures when tuned correctly, bart proved useful for identifying side keywords and entities. on the other hand, the generated content from all models lacked originality. further analysis and experimentation are required to determine the full potential of each model and its suitability for specific tasks.