While not long ago probabilistic autoregressive language models were just models that assign probabilities to sequences of words (Bahl et al., 1983), now they are the cornerstone of any task in computational linguistics through prompting (Sanh et al., 2022) or fine-tuning (Radford et al., 2018). Such models being successfully commercialized, the number of practical applications of these models is rapidly growing, as is the number of papers considering various aspects of the use of probabilistic autoregressive language models. It is all the more surprising that the statistical properties of the output sequences produced by such models have been studied relatively little. We aim to fill this gap somewhat and empirically demonstrate that, depending on the temperature parameter, LLMs can generate text that can be classified as solid (periodic phase), critical state (that has autocorrelations decay according to the power law) or gas (amorphous phase) from the point of view of autocorrelation analysis. Our main contributions are the following: 1. We clearly identify three phases of LLM-generated texts - periodic, critical and amorphous 2. We show through computational experiments that for LLM-generated texts, there is a phase transition from ordered to amorphous state at about the same temperatures between 0.7 and 1, for different LLMs 3. We show that for amorphous state, long-range autocorrelations decay follows the exponential law independently from the generation temperature, for different LLMs 4. We show that for temperatures between 0.7 and 1 autocorrelations exhibit power law decay on medium distances of up to 2000 words, implying isles of connectivity of these sizes. We go on to introduce the key concepts.

General large language models (LLMs) such as ChatGPT have shown remarkable success, but it has also raised concerns among people about the misuse of AI-generated texts. Therefore, an important question is how to detect whether the texts are generated by ChatGPT or by humans. Existing detectors are built on the assumption that there is a distribution gap between human-generated and AI-generated texts. These gaps are typically identified using statistical information or classifiers. In contrast to prior research methods, we find that large language models such as ChatGPT exhibit strong self-consistency in text generation and continuation. Self-consistency capitalizes on the intuition that AI-generated texts can still be reasoned with by large language models using the same logical reasoning when portions of the texts are masked, which differs from human-generated texts. Using this observation, we subsequently proposed a new method for AI-generated texts detection based on self-consistency with masked predictions to determine whether a text is generated by LLMs. This method, which we call DetectGPT-SC. We conducted a series of experiments to evaluate the performance of DetectGPT-SC. In these experiments, we employed various mask scheme, zero-shot, and simple prompt for completing masked texts and self-consistency predictions. The results indicate that DetectGPT-SC outperforms the current state-of-the-art across different tasks.

Can generative AI ChatGPT really serve as a mirror into humanity? Mirror, mirror, on the wall -- humans are the brightest of them all! That isn't of course a proper quotation from the famed Snow White and the Seven Dwarfs, but I opted to leverage the contrivance for a handy purpose. The matter has to do with how humankind sees itself when looking in an all-seeing all-telling mirror. Are we the cat's meow? Do we stand tall above all else? The reason I bring this up has to do with a topic that at first glance might seem afield of the weighty matters underlying how humankind perceives its place in the cosmos. I am going to tie these big-time vexing questions about life, our existence, and humanity all told to the emergence of Artificial Intelligence (AI). Some are insisting that the latest in AI can serve as a mirror into the soul of humanity. Yikes, do we want this? Maybe we won't like what we see. On the other hand, perhaps we have to stiffen our resolve and use AI to see us as we really are. Like a bucket of ice-cold water, AI might be the right thing at the right time to shock us into realizing who we are and where we are going.