TL;DR
This study introduces the Artificial Intelligence Scientific Text Detector, a hybrid model combining BigBird embeddings with a CNN-BiLSTM framework that can effectively identify LLM-generated content, supporting the integrity and credibility of academic publications.
Experimental results demonstrate ATDPS's superior performance over existing baselines in terms of accuracy, precision, recall and F1 score, highlighting its potential and effectiveness in detecting AI-generated text.
A novel paradigm called Human vs. LLM Identification (HLI) is proposed which introduces a Retrieval-Augmented Generation (RAG)-inspired evidence-based detection strategy alongside a fine-tuned transformer classifier.
A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts. They are the basis for many modern chatbots, such as Chat...
A novel method named IED is proposed, which leverages the information gain to construct the vector of which each dimension represents the information entropy of each word, and then adopts a classifier to conduct the detection.
Tanglish, that is, the use of Tamil language with Latin alphabet and English words sprinkled throughout, is considered the prevalent style of texting and commenting among the Tamil community in the online environment. Sarcasm detection in this form of language is especially difficult through automatic processes due to...
The widespread adoption of large language models (LLMs) has made AI-generated text increasingly difficult to distinguish from human writing, even in languages previously considered beyond the reach of generative AI. For Khasi, an Austroasiatic language spoken in Meghalaya, India, this presents a pressing problem: no pr...
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