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arXiv · 2403.19354

AIpom at SemEval-2024 Task 8: Detecting AI-produced Outputs in M4

Alexander Shirnin·Nikita Andreev·Vladislav Mikhailov·Ekaterina Artemova
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Abstract

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.

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Alexander Shirnin, Nikita Andreev, Vladislav Mikhailov, Ekaterina Artemova. 2024-03-28. AIpom at SemEval-2024 Task 8: Detecting AI-produced Outputs in M4. https://arxiv.org/abs/2403.19354

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