Mar 13, 2024 · Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, …

Mar 3, 2025 · True and false positives and negatives are used to calculate several useful metrics for evaluating models. Which evaluation metrics are most meaningful depends on the specific …

key element of text, we hereby propose a Character-Level Evaluation metric (CLEval). In CLEval, the instance match-ing process handles split and merge detection cases, and the scoring …

Oct 24, 2023 · We released DetectRL, a benchmark for real-world LLM-generated text detection, provide real utility to researchers on the topic and practitioners looking for consistent …

Jan 1, 2022 · Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression (LR), Multinomial Naïve Bayes (MNB), and Random Forest (RF) are Machine Learning based …

With such motivation, this article presents a novel metric learning paradigm for detection of LLM-generated texts that is able to balance computational costs, accessibility, and performances.

Thus, we propose a systematic review of anomaly detection solutions in the text. To do so, we analyze the included papers in our survey in terms of anomaly detection types, feature …

With the rise of AI-generated text, there is a growing need for robust detection models to identify such content accurately. This study aims to develop a machine learning-based system for …

To address these issues, researchers have considered automatic detection methods that can identify the machine-generated text (MGT) from the human-written text (HWT). Concretely, …

Sep 13, 2024 · In this blog, we will focus on some popular untrained metrics (with code) for evaluating the quality of text generated by existing Natural Language Generation (NLG) …