AIpom at SemEval-2024 Task 8: Detecting AI-produced Outputs in M4
This work addresses the challenge of identifying AI-generated content in mixed texts, which is important for applications like content moderation and authenticity verification, but it is incremental as it builds on existing methods.
The paper tackled the problem of detecting boundaries between human-written and machine-generated text in mixed texts, achieving a Mean Absolute Error of 15.94 and ranking second on the SemEval-2024 leaderboard.
This paper describes AIpom, a system designed to detect a boundary between human-written and machine-generated text (SemEval-2024 Task 8, Subtask C: Human-Machine Mixed Text Detection). We propose a two-stage pipeline combining predictions from an instruction-tuned decoder-only model and encoder-only sequence taggers. AIpom is ranked second on the leaderboard while achieving a Mean Absolute Error of 15.94. Ablation studies confirm the benefits of pipelining encoder and decoder models, particularly in terms of improved performance.