AIpom at SemEval-2024 Task 8: Detecting AI-produced Outputs in M4
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911818447847424 |
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| author | Shirnin, Alexander Andreev, Nikita Mikhailov, Vladislav Artemova, Ekaterina |
| author_facet | Shirnin, Alexander Andreev, Nikita Mikhailov, Vladislav Artemova, Ekaterina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIpom at SemEval-2024 Task 8: Detecting AI-produced Outputs in M4 Shirnin, Alexander Andreev, Nikita Mikhailov, Vladislav Artemova, Ekaterina Computation and Language 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. |
| title | AIpom at SemEval-2024 Task 8: Detecting AI-produced Outputs in M4 |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.19354 |