Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?

Advances in natural language processing (NLP) have been driven mainly by scaling up the size of pre-trained language models, along with the amount of data and compute required for training (Raffel et al., 2020; Radford et al., 2019; Rae et al., 2021; Fedus et al., 2021; Hoffmann et al., 2022). OpenAI recently released ChatGPT, a text generation model with conversational capabilities. The model is based on GPT3.5 which is a version of GPT3 (Brown et al., 2020) first fine-tuned on code then fine-tuned using Reinforcement Learning from Human Feedback (RLHF) (Christiano et al., 2017; Stiennon et al., 2020), a method previously demonstrated by OpenAI with Instruct-GPT (Ouyang et al., 2022). This fine-tuning process contributes not only to the model's knowledge but also simplifies the model's interface compared to GPT3, which necessitated substantial prompt engineering to achieve satisfactory outcomes, and hence facilitating the extraction and application of that built-in knowledge. As a result of these significant performance improvements, ChatGPT and other large language models have gained much popularity in the media and in the social context, often without fully understanding the underlying limitations of the models - e.g., the possibility of generating hateful, hateful, toxic, or disrespectful content (Bender et al., 2021; McGuffie & Newhouse, 2020; Weidinger et al., 2021). Another potential misuse of LLMs or ChatGPT is industrializing radicalization and harmful propaganda which poses a significant and unconventional threat to civil society. In response to the mounting concerns surrounding potential misuse, numerous researchers are now exploring various strategies to mitigate associated risks.