HULLMI: Human vs LLM identification with explainability

Prathamesh Dinesh Joshi, Sahil Pocker, Raj Dandekar, Rajat Dandekar, Sreedath Panat

Abstract:

As LLMs become increasingly proficient at producing human-like responses, there has been a rise of
academic and industrial pursuits dedicated to flagging a given piece of text as "human" or "AI". Most
of these pursuits involve modern NLP detectors like T5-Sentinel and RoBERTa-Sentinel, without
paying too much attention to issues of interpretability and explainability of these models. In our
study, we provide a comprehensive analysis that shows that traditional ML models (Naive-Bayes,
MLP, Random Forests, XGBoost) perform as well as modern NLP detectors, in human vs AI text
detection. We achieve this by implementing a robust testing procedure on diverse datasets, including
curated corpora and real-world samples. Subsequently, by employing the explainable AI technique
LIME, we uncover parts of the input that contribute most to a model’s prediction, providing insights
into the detection process. Our study contributes to the growing need for developing production-level
LLM detection tools, which can leverage a wide range of traditional as well as modern NLP detectors
we propose. Finally, the LIME techniques we demonstrate also have the potential to equip these
detection tools with interpretability analysis features, making them more reliable and trustworthy in
various domains like education, healthcare, and media.