🔎 AIGC Detection¶

🤖 AAAI2026 · 2 paper notes

BAID: A Benchmark for Bias Assessment of AI Detectors: Proposes the BAID benchmark dataset (208k sample pairs covering 7 bias dimensions and 41 subgroups) to systematically evaluate the fairness performance of four open-source AI text detectors across various demographic and linguistic subgroups, revealing significant recall disparities of detectors on dialects, informal English, and minority texts.
Optimized Algorithms for Text Clustering with LLM-Generated Constraints: Proposes the LSCK-HC framework, which utilizes LLMs to generate must-link/cannot-link constraints in set formats (rather than traditional pairwise constraints), and integrates with a penalized local search clustering algorithm. It achieves clustering accuracy comparable to SOTA on 5 short text datasets while reducing the number of LLM queries by over 20 times.