- Updated: January 30, 2026
- 1 min read
On the Effectiveness of LLM-Specific Fine-Tuning for Detecting AI-Generated Text
The rapid progress of large language models has enabled the generation of text that closely resembles human writing, creating challenges for authenticity verification in education, publishing, and digital security. Detecting AI-generated text has therefore become a crucial technical and ethical issue. This paper presents a comprehensive study of AI-generated text detection based on large‑scale corpora and novel training strategies. We introduce a 1‑billion‑token corpus of human‑authored texts spanning multiple genres and a 1.9‑billion‑token corpus of AI‑generated texts produced by prompting a variety of LLMs across diverse domains. Using these resources, we develop and evaluate numerous detection models and propose two novel training paradigms: Per LLM and Per LLM family fine‑tuning. Across a 100‑million‑token benchmark covering 21 large language models, our best fine‑tuned detector achieves up to 99.6% token‑level accuracy, substantially outperforming existing open‑source baselines.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.