AAIG at GenAI Detection Task 1: Exploring Syntactically-Aware, Resource-Efficient Small Autoregressive Decoders for AI Content Detection

Avanti Bhandarkar, Ronald Wilson, Damon L. Woodard

Abstract: This paper presents a lightweight and efficient approach to AI-generated content detection using small autoregressive fine-tuned decoders (AFDs) for secure, on-device deployment. Motivated by resource-efficiency, syntactic awareness, and bias mitigation, our model employs small language models (SLMs) with autoregressive pre-training and loss fusion to accurately distinguish between human and AI-generated content while significantly reducing computational demands. The system achieved highest macro-F1 score of 0.8186, with the submitted model scoring 0.7874—both significantly outperforming the task baseline while reducing model parameters by ~60%. Notably, our approach mitigates biases, improving recall for human-authored text by over 60%. Ranking 8th out of 36 participants, these results confirm the feasibility and competitiveness of small AFDs in challenging, adversarial settings, making them ideal for privacy-preserving, on-device deployment suitable for real-world applications.