Flag AI-Generated Text with Hive’s New Classifier

Hive is excited to announce our new classifier to differentiate between AI-generated and human-written text. This model is hosted on our website as a free demo, and we encourage users to test out its performance.

The recent release of OpenAI’s ChatGPT model has raised questions about how public access to these kinds of large language models will impact the field of education. Certain school districts have already banned access to ChatGPT, and teachers have been adjusting their teaching methods to account for the fact that generative AI has made academic dishonesty a whole lot easier. Since the rise of internet plagiarism, plagiarism detectors have become commonplace at academic institutions. Now a need arises for a new kind of detection: AI-generated text.

Our AI-Generated Text Detector outperforms key competitors, including OpenAI itself. We compared our model to their detector, as well as two other popular AI-generated text detection tools: GPTZero and Writer’s AI Content Detector. Our model was the clear frontrunner, not just in terms of balanced accuracy but also in terms of false positive rate — a critical factor when these tools are deployed in an educational setting.

Our test dataset consisted of 242 text passages, including ChatGPT-generated text as well as human-written text. To ensure that our model behaves correctly on all genres of content, we included everything from casual writing to more technical and academic writing. We took special care to include texts written by those learning English as a second language, so as to be careful that their writing is not incorrectly categorized by our model due to differences in tone or wording. For these test examples, our balanced accuracy stands at an impressive 99% while the closest competitor is GPTZero with 83%. OpenAI got the lowest of the bunch, with only 73%.

Others have tried our model against OpenAI’s in particular, and they have echoed our findings. Following OpenAI’s classifier release, Mark Hachman at PCWorld published an article that suggested that those disappointed with OpenAI’s model should turn to Hive’s instead. In his own informal testing of our model, he praised our results for their accuracy as well as our inclusion of clear confidence scores for every result.

A large fear about using these sorts of detector tools in an educational setting is the potentially catastrophic impact of false positives, or cases in which human-written writing is classified as AI-generated. While building our model, we were mindful of the fact that the risk of such high-cost false positives is one that many educators may not want to take. In response, we prioritized lowering our false positive rate. On the test set above, our false positive rate is incredibly low, at 1%. This is compared to OpenAI’s at 12.5%, Writer’s at 46%, and GPTZeros at 30%.

Even with our low false positive rate, we do encourage that this tool be used as part of a broader process when investigating academic dishonesty and not as the sole decision maker. Just like plagiarism checkers, it is created to be a helpful screening tool and not a final judge. We are continuously working to improve our model, and any feedback is greatly appreciated. Large language models like ChatGPT are here to stay, and it is crucial to provide educators with tools they can use as they decide how to navigate these changes in their classrooms.