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Overlap, Unique and Conflict: Can LLMs Extract What They Can Recognize? — arXiv:2609.38799

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.38799
License (as archived): CC0-1.0
Archived: 2026-10-07T13:13:31.131Z
SHA-256 of the archived text: a7f1a3b56743772f3d492629f1bd67d74263f3e59cb214ccf6a8727b64951b16
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How to cite

Overlap, Unique and Conflict: Can LLMs Extract What They Can Recognize? — arXiv:2609.38799 — arXiv (en), https://arxiv.org/abs/2609.38799 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a01fcd425fc0 (Metraipolis archive dc_a01fcd425fc0, sha256 a7f1a3b56743772f3d492629f1bd67d74263f3e59cb214ccf6a8727b64951b16)

BibTeX

@misc{dca01fcd425fc0,
  title = {Overlap, Unique and Conflict: Can LLMs Extract What They Can Recognize? — arXiv:2609.38799},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.38799},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a01fcd425fc0; sha256 a7f1a3b56743772f3d492629f1bd67d74263f3e59cb214ccf6a8727b64951b16},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.38799. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Understanding multi-perspective alternative narratives requires identifying how their information agrees, conflicts, or differs across sources. Existing work on cross-text relations largely focuses on categorizing relations between predefined text pairs, such as entailment or contradiction, rather than directly extracting such information from full narratives. To address this gap, we introduce Overlap-Unique-Conflict (OUC) extraction, a cross-narrative task that extracts all overlapping, conflicting, and unique clauses from two narratives. To support this study, we construct a benchmark of approximately 22K narrative pairs and 140K OUC instances spanning factual, argumentative, and political discourse. Evaluating 14 open-source LLMs (0.6B-35B), we find that unique information is far easier to extract than overlap and conflict: the strongest model, Gemma-4-31B, reaches only 61.13% F1-score on overlap and 48.58% on conflict, against more than 75% on unique. Further diagnostic analysis reveals that this difficulty does not stem from relation recognition alone, but rather from a failure to pair and extract the corresponding clauses from full narratives, especially in smaller models. Nevertheless, learning these extractions with task-specific supervision narrows the gap considerably: a fine-tuned Qwen-3-8B gains 15-28% absolute over its baseline and surpasses models roughly four times its size (e.g., Qwen-3.6-35B) on several tasks. Even so, overlap and conflict remain well below satisfactory, leaving cross-narrative clause extraction an open challenge.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.38799 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:44:51.758Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).