From Text to Source: Results in Detecting Large Language Model-Generated Content

in Scientific publications
Share this publication
Author Wissam Antoun, Benoît Sagot, Djamé Seddah
Title of Journal, Proc. or Book COLING 2024
Issue September 2023
Repository link
Open access Yes

The widespread use of Large Language Models (LLMs), celebrated for their ability to generate human-like text, has raised concerns about misinformation and ethical implications. Addressing these concerns necessitates the development of robust methods to detect and attribute text generated by LLMs. This paper investigates “Cross-Model Detection,” evaluating whether a classifier trained to distinguish between source LLM-generated and human-written text can also detect text from a target LLM without further training. The study comprehensively explores various LLM sizes and families, and assesses the impact of conversational fine-tuning techniques on classifier generalization. The research also delves into Model Attribution, encompassing source model identification, model family classification, and model size classification. Our results reveal several key findings: a clear inverse relationship between classifier effectiveness and model size, with larger LLMs being more challenging to detect, especially when the classifier is trained on data from smaller models. Training on data from similarly sized LLMs can improve detection performance from larger models but may lead to decreased performance when dealing with smaller models. Additionally, model attribution experiments show promising results in identifying source models and model families, highlighting detectable signatures in LLM-generated text. Overall, our study contributes valuable insights into the interplay of model size, family, and training data in LLM detection and attribution.

Previous Post
Data-Efficient French Language Modeling with CamemBERTa
Next Post
Greedy routing optimisation in hyperbolic networks
You may also be interested in these topics
Skip to content