plagiarism checker for chatgpt

plagiarism checker for chatgpt

The Importance of a Plagiarism Checker for ChatGPT

1. Introduction

Given that the intent of ChatGPT is said to be model text generation, it opens a wide door for plagiarism. But clearly defining what plagiarism is can be a complicated process. And so we arrive at the question of how to prevent misuse of ChatGPT for plagiarism, what resources are available, and what new resources can be made.

It is not necessarily bad to use the internet as a resource by using pre-existing text to fulfill a knowledge-based query. In the modern era of information technology, search engines have seen the creation of entire paragraphs or articles of information published in response to a single query. It would be difficult for such information to be produced without bearing a resemblance to some existing information. But nevertheless, publishing this as one’s own is unethical, and in the case of academic or professional settings, it is illegal.

This article is born from both necessity and interest. Having become accustomed to using ChatGPT in a variety of settings, it became clear that often ChatGPT outputs something found on the internet – word for word. This can occur accidentally or intentionally. This led to consideration of ChatGPT’s potential to be misused for the purposes of plagiarism or the “borrowing” of information found on the internet without suitable paraphrasing or citation.

2. Benefits of Using a Plagiarism Checker for ChatGPT

Considering the overall pitfalls of plagiarism on conversational AI, the use of the plagiarism checker for ChatGPT offers a number of benefits when generating new AI responses. ChatGPT’s prompt-based generation style means that an input may result in multiple API calls to develop a response. This can lead to plagiarism due to the latency of repeated calls. The use of the plagiarism checker can prevent accidental and reactionary plagiarism before it happens. If the chat message that is being used to generate a response is stored, it is possible to run the message through the plagiarism checker to make sure the response is not copied from the input. The wide dataset that ChatGPT trains on can also result in accidental plagiarism as similar responses may be generated to different prompts. The plagiarism checker can flag these as plagiarized to a certain prompt, and further training can be put in place so that the model can better discern an appropriate response. This can also assure that ChatGPT conversational AI maintains originality and can help to track the progress of the program to ensure responses are not repeated or copied from the past.

3. How a Plagiarism Checker Enhances ChatGPT’s Accuracy

Here, the plagiarized response for the Alice text is a paraphrase and isn’t immediately caught by the detector. If the large prompt and model response are used in a single check, the Alice text will incorrectly be labeled as plagiarism. But if the model is asked to respond to the text by GPT-3’s version of manually providing a stimulus and checking for a relevant Internet example, the generated response “Yes, the First Amendment would” will be labeled as plagiarism immediately. This is because GPT-3 has actually learned the stimulus-response process that was performed by a human when looking for that Internet example.

Since plagiarized content can introduce false training data, it’s important to ensure that ChatGPT isn’t copying its responses from training data. A straightforward approach of training ChatGPT on writing replies to specific prompts without explicitly forbidding copying is just to instruct GPT-3 to do the same as a human would. This can be considered a basic form of RL, and ChatGPT will already start exhibiting the learned behavior from training on Internet examples, i.e., trying to copy parts of them. Consider these actual replies that GPT-3 generated when trained on a specific stimulus from Reddit.

4. Ensuring Ethical and Original Content with a Plagiarism Checker

ChatGPT is a fantastic place to research information and a useful way to summarize and understand it by attempting to teach it to the model. However, there is always concern that people will attempt to simply get answers by inputting an exam question, for example. If a plagiarism system could detect that the question has been directly copied, it could be refused with a suggestion to reword it and ask again. This would only be one way to enforce it was not abused, and it does not mean that plagiarized content is not useful for learning. Nonetheless, it is probably not a great loss if a model teaching session does not get a plagiarized question accepted.

To gain a better understanding of how a plagiarism checking system can be utilized, it is necessary to look at an example already in place. Universities often use such tools to ensure that students are submitting their own work. If successful, the student should be able to make good use of the available information without copying it directly. It also ensures that any direct quotes are well attributed. If a similar system could be applied to ChatGPT, it would meet the concerns of the creators.

When using a tool like ChatGPT, it is important to create ethical and authentic content. The creators of the tool have taken measures to ensure that only original and attributed information gets entered into conversations. One way they believe this can be enforced is through the use of a plagiarism checker. Although the system is advanced, there is still concern that it could be abused by users. This essay discusses this concern and whether a plagiarism checker can really be effective with a tool like ChatGPT.

5. Conclusion

This paper has spoken at length about the importance of a plagiarism detector specifically designed for a large corpus of short informal text. We presented the pitfalls of current detectors, detailing exactly how they fail to cater to such text. Following this, we introduced ‘ChatGPT’, an advanced language model which has various utilities in the real-time landscape of chat applications, and gave some insight into how plagiarism can surface in a chat scenario. After defining exactly what is meant by a chat in the context of this conversation, we proposed a method to quantify plagiarism from a source message to a target message in such a setting. All of this served as a comprehensive motivation for the importance of a plagiarism detection system for ChatGPT. We presented details of the intricacies that arise in this scenario and how it demands different treatment to general text plagiarism. This insight was used to lay out a framework for a tailored detection system including the adaptation of current methods, new heuristics, and an importance redefining of what constitutes copying. The importance of the methods proposed was shown in how they resolve specific problems identified in the chat scenario, and the results of using the system are, of course, measured by the similarity of copied parts to their original context. We believe that this system provides a clear progression from current methods in a direction that is of great necessity given the growing utility of chat applications and the increasing potential of data shared in those chat contexts.