can plagiarism checker detect chat gpt

can plagiarism checker detect chat gpt

The Effectiveness of Plagiarism Checkers in Detecting Chat GPT

1. Introduction

This highlights an important general issue concerning the use of plagiarism detection on GPT-prepared text: not all instances of copied text are indicative of intentional plagiarism, and it is important to consider the intent of the user and the target context for their text.

The detection of paraphrased text is already a task with some difficulty, and assessing whether a piece of text is too similar to its original is a subjective task in itself. In the case where intentional rewording is used by a student to summarize an external piece of text, it is possible that a plagiarism detection tool may return a false positive for copying. It is important for the detector to know the context for the text it is checking and differentiate between direct quotes that require citation and reworded sections that are summarizations. A student who is unaware of the capability of plagiarism detection software may be wrongfully accused of plagiarizing in this scenario.

If a student is trying to fabricate an essay to submit to their teacher, it is likely that the essay needs to maintain high quality and coherency with the minimum possible direct copying. This process is best approached using a detection method that can accurately pinpoint plagiarized sections and indicate to the user whether rewordings are sufficient. An automatic detector would then be a preferable tool to a human in this case, as it can do the checking quickly with high consistency. This may aid in the detection of deliberate plagiarism using GPT and act as a deterrent to its use in cases where the risk of detection is too high.

GPT has the advantage of being able to generate an almost unlimited variety of semantically rich sentences for any given input, so creating a modified version of some piece of text can be done as easily as simply generating a new piece of text. However, this does not make the task impossible for a plagiarism detector. There is a trade-off for the user between the amount of modification to the text and retention of original content, and the likelihood that highly modified text will still adequately serve its purpose.

There is a fair amount of similarity between the proposed task and the use of paraphrasing in an attempt to avoid the detection of directly copied text. The goal in both cases is to evade text-based detection of copying while retaining as much of the original meaning and content as possible. Therefore, it is reasonable to assume that if standard methods are ineffective against advanced plagiarism using GPT, then the task may also be difficult for a plagiarism detector.

2. Understanding Chat GPT

Section is a modern generation linguistic and conversation model developed by OpenAI in 2019. Chat GPT, as the name suggests, works by assuming quite a few turns of conversation from the user it engages with and attempting to respond appropriately in the context of the conversation at hand. Airlines have trained Chat GPT models on a collection of past conversations with customers to enable the model to act as a human inquired about a given context. Despite the advances in language models at large with models like BERT and large improvements in automation of customer service and sales through non-conversational GPT models, these tools are still behind humans in the production of effective context-oriented conversations. This is done in research for tools to further automation of chat-oriented tasks, where GPT models of the current are trying to emulate and improve upon themselves in conversation using themselves in context to always respond to users appropriately. In the nature of checking the effectiveness of plagiarism checkers, the author assumes that recent students and researchers may have written AI scripts for chat-oriented tasks which may involve the generation of text. With there being an ultimate goal to automate customer service or intelligent conversational tasks on the cheap using an AI present, it is certain that an improved plagiarism checker will be needed along the development of conversational AI. This more advanced check being done in the future work of this model is a valuable pre-existing tool for the purpose of altering and generating specific text (i.e. text with a certain AI agent character and dialect).

3. Limitations of Plagiarism Checkers

It has been further proposed by Wise (2010) that machine translation, the act of translating a piece of text from one natural language to another using a computer program, has the potential to allow students to plagiarize foreign language material. This is because the quality of the translation may convince an educator that the student has a high level of understanding of the foreign language and would therefore be able to write text of the same quality themselves. This is concerning as human rating of machine translations has been shown to be unreliable at identifying whether a piece of text is a machine translation or a human translation. This would mean that an educator wishing to verify a suspicion may have to translate the foreign language text back to the original language and then use a plagiarism checker on the translation to confirm. This is a time-consuming task, and it is unlikely that educators would be thorough in verification for all suspicious pieces of foreign language text.

Despite the effectiveness of plagiarism checkers in detecting certain forms of plagiarism (mainly copy and paste), they have limitations. According to Baldwin (2018), some internet paper mills use sophisticated algorithms and programming to “scramble” the code, i.e., the paper, so that it appears the same to the human eye but different to software used by plagiarism checkers. A quality example of such software is generated by GPT-2. This model can take input on almost any topic and produce natural and coherent text. It is more realistic than previous models and has been trained on a broad range of internet text. Research by OpenAI, who developed the model, showed that the authenticity of generated text makes it difficult for unaided human raters to distinguish between real and computer-generated text. They propose that future iterations of such models could be used by spammers to generate authentic-looking content with backlinks to their sites to improve search engine optimization.

4. Challenges in Detecting Chat GPT Plagiarism

Detecting plagiarism in Chat GPT presents a number of unique challenges above and beyond those faced in traditional plagiarism detection. Chat GPT models can plagiarize information from online sources in a simple manner by cutting and pasting. However, Chat GPTs are more likely to modify the text, and therefore the original text is not verbatim present in the response. A successful Chat GPT may also spontaneously generate text similar to online sources, therefore there may not actually exist a similar sentence in the source material. Finally, given that many online sources are based on information that is largely sociocultural or scientific, it is quite likely that a GPT which has been trained on such material will produce text similar to the source, which is not necessarily plagiarized. An ideal plagiarism detection system should account for all these scenarios. It should be consistent in the way it judges plagiarism and produce relative assessments between similar pieces of text. It should be sensitive in differentiating between random text generation which happens to be similar to a source and actual plagiarism. Also, an ideal system for plagiarism detection should strive to minimize false positives and negatives. This is the measure of a system’s ability to correctly detect whether or not a given piece of text is plagiarized, and it is a function of both the system’s sensitivity and its consistency.

5. Enhancing Plagiarism Checkers for Chat GPT Detection

To avoid privacy issues arising from data being sent to external servers, this method of plagiarism detection would be limited to checking chat similarity within the specific application.

Consider a scenario in which a teacher sets an assignment to provide a humanly response to certain conversation stimuli and pays for them to be implemented into a chatbot for a language learning tool. The chat logs from the conversation could be used to identify that the chatbot developer has plagiarized the teacher’s responses by comparing them to the stimulus using similarity measures and paraphrase identification techniques. These methods can also be applied to search engines to locate similar phrases and pages on the internet.

Checkers could make modules to store chat logs and canned responses in persistent memory storage and assign an expiry to how often that data is updated. When a GPT responds with an existing phrase, it will be able to compare back to the memory storage and identify that it is the same response as someone else.

Current chat GPT responses only provide a single turn response and are not designed to remember past chat sessions. Plagiarism detection for chat GPT should be able to differentiate between copying someone else’s response and using a canned response because it felt like the appropriate reply at the time. The latter is not actually plagiarizing chat since that response isn’t from the person whom you are chatting with.

Plagiarism checkers could be further developed for chat GPT detection, considering the asynchronous and multi-party nature of chat communication. Since there are two parties involved in the chat and the response time is not immediate, the conversation could span multiple sessions in reality.