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Decoding the AI Pen: Techniques and Challenges in Detecting AI-Generated Text

Year: 2024

Type: conference-paper

Abstract: Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their widespread usage introduces challenges that necessitate thoughtful examination, ethical scrutiny, and responsible practices. In this study, we delve into these challenges, explore existing strategies for mitigating them, with a particular emphasis on identifying AI-generated text as the ultimate solution. Additionally, we assess the feasibility of detection from a theoretical perspective and propose novel research directions to address the current limitations in this domain.

Source: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/3637528.3671463

Authors: Sara Abdali, Richard Anarfi, CJ Barberan, Jia He

Institution: Microsoft (United States)

Language: English

Topic: Authorship Attribution and Profiling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4392736762

FWCI: 5.772

Cites: 7

Cited by: 19

Related to: 10