These can range from logistic regression models to convolutional In this paper, we study the problem of detecting neural networks (Weller and Woo, 2019) or LSTM models machine-generated text when the large language model (Kudugunta and Ferrara, 2018). These binary classifiers (LLM) it is possibly derived from is unknown. We do so by can also act as base learners in ensemble methods (Fayaz et apply ensembling methods to the outputs from DetectGPT al., 2020). These features can also be augmented with classifiers (Mitchell et al. 2023), a zero-shot model for additional information such as account data in the context machine-generated text detection which is highly accurate of social media bot detection. However, high classification when the generative (or base) language model is the same accuracy for these methods are reliant on sufficiently-long as the discriminative (or scoring) language model. We find text length and a sufficiently-diverse corpus of training that simple summary statistics of DetectGPT sub-model machine-generated samples in terms of stylometric and outputs yield an AUROC of 0.73 (relative to 0.61) while linguistic characteristics in order to prevent overfitting. As retaining its zero-shot nature, and that supervised learning such, these classifiers need to be continually trained and methods sharply boost the accuracy to an AUROC of 0.94 updated, limiting their usefulness (Pegoraro et al., 2023).

In today's machine learning (ML) models, any part of the training data can affect its output. This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access control only allows individual users to access a subset of data. To enable secure machine learning for access controlled data, we propose the notion of information flow control for machine learning, and develop a secure Transformer-based language model based on the Mixture-of-Experts (MoE) architecture. The secure MoE architecture controls information flow by limiting the influence of training data from each security domain to a single expert module, and only enabling a subset of experts at inference time based on an access control policy. The evaluation using a large corpus of text data shows that the proposed MoE architecture has minimal (1.9%) performance overhead and can significantly improve model accuracy (up to 37%) by enabling training on access-controlled data.

In the previous article, we talked about the way that powerful type of Recurrent Neural Networks – Long Short-Term Memory (LSTM) Networks function. They are not keeping just propagating output information to the next time step, but they are also storing and propagating the state of the so-called LSTM cell. This cell is holding four neural networks inside – gates, which are used to decide which information will be stored in cell state and pushed to output. So, the output of the network at one time step is not depending only on the previous time step but depends on n previous time steps. Ok, that is enough to get us up to speed with theory, and prepare us for the practical part – implementation of this kind of networks.

In a pair of previous posts, we first discussed a framework for approaching textual data science tasks, and followed that up with a discussion on a general approach to preprocessing text data. This post will serve as a practical walkthrough of a text data preprocessing task using some common Python tools. Our goal is to go from what we will describe as a chunk of text (not to be confused with text chunking), a lengthy, unprocessed single string, and end up with a list (or several lists) of cleaned tokens that would be useful for further text mining and/or natural language processing tasks. First we start with our imports. If you have NLTK installed, yet require the download of its any additional data, see here.

In the past few days, I've taught a machine learning algorithm how to write in the style of Harry Potter, Hamilton (the musical), and HBO's Silicon Valley. The mostly non-sensical, occasionally human-like, topically-flavored writing seems to be amusing not only to me, but to many others. Thus, I've made this quick tutorial to teach you how to create your own instances of "Deep Writing". This is not going to be an in-depth description of the underlying technology -- but instead, a step-by-step guide that anybody can follow (even if you have no coding or machine learning experience). Here is a very crude approximation of what is involved in the Deep Writing process. More than anything, this is meant to give you enough intuition and appreciation to follow along with the rest of the tutorial.

Computational indices related to n-gram production were developed in order to assess the potential for n-gram indices to predict human scores of essay quality. A regression analyses was conducted on a corpus of 313 argumentative essays. The analyses demonstrated that a variety of n-gram indices were highly correlated to essay quality, but were also highly correlated to the number of words in the text (although many of the n-gram indices were stronger predictors of writing quality than the number of words in a text). A second regression analysis was conducted on a corpus of 88 argumentative essays that were controlled for text length differences. This analysis demonstrated that n-gram indices were still strong predictors of essay quality when text length was not a factor.

The use of statistical AI techniques in authorship recognition (or stylometry) has contributed to literary and historical breakthroughs. These successes have led to the use of these techniques in criminal investigations and prosecutions.  However, few have studied adversarial attacks and their devastating effect on the robustness of existing classification methods. This paper presents a framework for adversarial attacks including obfuscation attacks, where a subject attempts to hide their identity imitation attacks, where a subject attempts to frame another subject by imitating their writing style.  The major contribution of this research is that it demonstrates that both attacks work very well.  The obfuscation attack reduces the effectiveness of the techniques to the level of random guessing and the imitation attack succeeds with 68-91% probability depending on the stylometric technique used.  These results are made more significant by the fact that the experimental subjects were unfamiliar with stylometric techniques, without specialized knowledge in linguistics, and spent little time on the attacks. This paper also provides another significant contribution to the field in using human subjects to empirically validate the claim of high accuracy for current techniques (without attacks) by reproducing results for three representative stylometric methods.