mlsanityData to 14 Oct 2026

Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview

arXiv 2412.03933 ↗First posted 5 Dec 2024cs.AI cs.HC cs.LGcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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 detection tools that help differentiate AI-generated text from human-written content and discusses the ethical challenges these technologies pose. The paper explores future directions for improving detection accuracy, supporting ethical AI development, and increasing accessibility. The paper contributes to a more responsible and reliable use of AI in content creation through these discussions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 35 references that are in the map. First posted on arXiv 5 Dec 2024.

Builds on

12 of 35 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  5. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  6. 2021GPT Understands, Toocited by 716
  7. 2022Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Modelcited by 559
  8. 2023Do LLMs exhibit human-like response biases? A case study in survey designcited by 180
  9. 2023Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writerscited by 32
  10. 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
  11. 2017Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHCcited by 13
  12. 2023On pitfalls (and advantages) of sophisticated large language modelscited by 7

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Machine Generated Product Advertisements: Benchmarking LLMs Against Human Performancecited by 1

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  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey
  2. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
  3. 2025Information Retrieval in the Age of Generative AI: The RGB Model
  4. 2024Deploying Large Language Models With Retrieval Augmented Generation
  5. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  6. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  7. 2025A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
  8. 2024Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report