The proliferation of sophisticated large language models (LLMs) has precipitated a crisis in academic and professional integrity, prompting the development of an array of AI detection tools designed to distinguish machine-generated text from human writing. Systems such as Turnitin AI, GPTZero, and Originality.ai purport to identify the statistical hallmarks
The design of input prompts is a fundamental aspect of interacting with large language models (LLMs), shaping their ability to deliver precise and meaningful outputs. As these models grow in sophistication, the quality of prompts determines their success in meeting user needs across diverse applications. A clear prompt is essential
Prompt engineering is the science and art of crafting, testing, and optimising input queries to guide LLMs towards specific, high-quality outputs. Liu et al. (2023) describe it as an "empirical science" that combines linguistic precision with an understanding of model architectures, transforming human intent into machine action. Unlike
Bias in artificial intelligence systems has become a critical concern as these technologies increasingly influence decision-making across domains such as healthcare, criminal justice, and employment. Bias manifests as systematic errors that lead to unfair or discriminatory outcomes, often disproportionately affecting marginalised groups. Understanding the origins of bias is essential for
Generative Artificial Intelligence (AI) has revolutionised digital media through its ability to synthesise highly realistic content, with deepfake technology standing as one of its most prominent and contentious applications. The term “deepfake,” derived from “deep learning” and “fake,” refers to synthetic media—typically videos or audio—that convincingly depict individuals
Misinformation, defined as false or misleading information disseminated regardless of intent, poses significant challenges to societal trust and democratic processes (Wardle & Derakhshan, 2017). Unlike disinformation, which involves deliberate deception, misinformation encompasses a broader spectrum, including unintentional errors, rumours, and misinterpretations. The advent GenAI models, capable of producing human-like text,
Content restriction in generative AI manifests as explicit or implicit censorship. Explicit censorship uses predefined rules to block content like hate speech or illegal material, employing keyword blacklists, pattern-matching, or classifiers (Gillespie 2018). DeepSeek’s models, aligned with Chinese regulations, use real-time filters to block politically sensitive content, such as
Detecting hallucinations involves distinguishing accurate outputs from those that deviate from factual or contextual grounding. One approach is consistency checking, where LLM outputs are evaluated against external knowledge bases to identify discrepancies. Manakul et al. (2023) propose SelfCheckGPT, a zero-resource method that uses the model’s internal consistency to detect
Advancements in artificial intelligence (AI), particularly in natural language processing (NLP), highlight critical distinctions between parroting and hallucination in language models. Parroting refers to AI reproducing or mimicking patterns and phrases from training data without demonstrating understanding or creativity. Hallucination involves generating factually incorrect, implausible, or fabricated outputs, often diverging
The rapid integration of Artificial Intelligence (AI) into global economies has driven transformative advancements in sectors such as healthcare and agriculture. However, this technological revolution incurs significant environmental costs, particularly through substantial energy consumption and greenhouse gas (GHG) emissions. The carbon footprint of AI, stemming from energy-intensive processes like hardware
Retrieval-Augmented Generation (RAG) represents a paradigm shift in natural language processing (NLP), integrating large language models (LLMs) with dynamic information retrieval systems to produce responses that are both contextually enriched and factually grounded (Lewis et al. 2020). At its core, the RAG architecture couples a conventional generative model—one that
Small Language Models (SLMs) are compact neural networks designed to perform natural language processing (NLP) tasks with significantly fewer parameters and lower computational requirements than their larger counterparts. SLMs aim to deliver robust performance in resource-constrained environments, such as mobile devices or edge computing systems, where efficiency is paramount. A
