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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Accès en ligne: | https://arxiv.org/abs/2602.01560 |
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| _version_ | 1866917287565459456 |
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| author | Inoshita, Keito Omura, Michiaki Yamanaka, Tsukasa Maeda, Go Tsuji, Kentaro |
| author_facet | Inoshita, Keito Omura, Michiaki Yamanaka, Tsukasa Maeda, Go Tsuji, Kentaro |
| contents | This study proposes Argument Rarity-based Originality Assessment (AROA), a framework for automatically evaluating argumentative originality in student essays. AROA defines originality as rarity within a reference corpus and evaluates it through four complementary components: structural rarity, claim rarity, evidence rarity, and cognitive depth, quantified via density estimation and integrated with quality adjustment. Experiments using 1,375 human essays and 1,000 AI-generated essays on two argumentative topics revealed three key findings. First, a strong negative correlation (r = -0.67) between text quality and claim rarity demonstrates a quality-originality trade-off. Second, while AI essays achieved near-perfect quality scores (Q = 0.998), their claim rarity was approximately one-fifth of human levels (AI: 0.037, human: 0.170), indicating that LLMs can reproduce argumentative structure but not semantic originality. Third, the four components showed low mutual correlations (r = 0.06--0.13 between structural and semantic dimensions), confirming that they capture genuinely independent aspects of originality. These results suggest that writing assessment in the AI era must shift from quality to originality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01560 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Argument Rarity-based Originality Assessment for AI-Assisted Writing Inoshita, Keito Omura, Michiaki Yamanaka, Tsukasa Maeda, Go Tsuji, Kentaro Computation and Language This study proposes Argument Rarity-based Originality Assessment (AROA), a framework for automatically evaluating argumentative originality in student essays. AROA defines originality as rarity within a reference corpus and evaluates it through four complementary components: structural rarity, claim rarity, evidence rarity, and cognitive depth, quantified via density estimation and integrated with quality adjustment. Experiments using 1,375 human essays and 1,000 AI-generated essays on two argumentative topics revealed three key findings. First, a strong negative correlation (r = -0.67) between text quality and claim rarity demonstrates a quality-originality trade-off. Second, while AI essays achieved near-perfect quality scores (Q = 0.998), their claim rarity was approximately one-fifth of human levels (AI: 0.037, human: 0.170), indicating that LLMs can reproduce argumentative structure but not semantic originality. Third, the four components showed low mutual correlations (r = 0.06--0.13 between structural and semantic dimensions), confirming that they capture genuinely independent aspects of originality. These results suggest that writing assessment in the AI era must shift from quality to originality. |
| title | Argument Rarity-based Originality Assessment for AI-Assisted Writing |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.01560 |