Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview
Fnu Neha, Deepshikha Bhati, Deepak Kumar Shukla, Angela Guercio, Ben, Ward

TL;DR
This paper provides a comprehensive overview of AI text generation, focusing on the evolution, capabilities, ethical issues, and recent advances like Retrieval-Augmented Generation, along with detection tools and future directions.
Contribution
It introduces Retrieval-Augmented Generation (RAG) as a novel approach to enhance text relevance and accuracy, and reviews detection tools and ethical considerations in AI text generation.
Findings
RAG improves contextual relevance and accuracy of AI-generated text.
Detection tools help differentiate AI from human-written content.
Ethical challenges include bias, misinformation, and accountability.
Abstract
The rapid development of Artificial Intelligence (AI) has led to the creation of powerful text generation models, such as large language models (LLMs), which are widely used for diverse applications. However, concerns surrounding AI-generated content, including issues of originality, bias, misinformation, and accountability, have become increasingly prominent. This paper offers a comprehensive overview of AI text generators (AITGs), focusing on their evolution, capabilities, and ethical implications. This paper also introduces Retrieval-Augmented Generation (RAG), a recent approach that improves the contextual relevance and accuracy of text generation by integrating dynamic information retrieval. RAG addresses key limitations of traditional models, including their reliance on static knowledge and potential inaccuracies in handling real-world data. Additionally, the paper reviews…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsKnowledge Management and Technology
MethodsAttention Is All You Need · Attention Dropout · Refunds@Expedia|||How do I get a full refund from Expedia? · Linear Layer · Softmax · Multi-Head Attention · Weight Decay · Byte Pair Encoding · WordPiece · Linear Warmup With Linear Decay
