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How broad is that claim? Mapping Generalisation in NLP Research — arXiv:2609.14770

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.14770
License (as archived): CC0-1.0
Archived: 2026-10-15T22:53:19.875Z
SHA-256 of the archived text: e8fa6d71a60497c4b3d678db6f2e5ab220bc171a7b738ad8679d83350f554c5c
Stable URL of this page: https://metraipolis.com/documento/dc_e81085df50aa

How to cite

How broad is that claim? Mapping Generalisation in NLP Research — arXiv:2609.14770 — arXiv (en), https://arxiv.org/abs/2609.14770 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e81085df50aa (Metraipolis archive dc_e81085df50aa, sha256 e8fa6d71a60497c4b3d678db6f2e5ab220bc171a7b738ad8679d83350f554c5c)

BibTeX

@misc{dce81085df50aa,
  title = {How broad is that claim? Mapping Generalisation in NLP Research — arXiv:2609.14770},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.14770},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e81085df50aa; sha256 e8fa6d71a60497c4b3d678db6f2e5ab220bc171a7b738ad8679d83350f554c5c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.14770. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generalisations are common in scientific communication, even though they are semantically ambiguous. An automated method is needed to identify and categorise claims according to their level of generalisation, in order help detect an over-reliance on generalisations and possible misrepresentations of scientific findings. We introduce a comprehensive taxonomy of generalisations in the scientific domain, NLPGenX, which labels claims according to their level of generality and framing within the text. We operationalise this taxonomy with an LLM-powered framework, NLPGenA, that automatically classifies sentences from scientific articles into 5 different generalisation classes. We validate our framework with human annotators and use the framework to construct a large-scale dataset of NLP papers annotated according to generality, with auxiliary labels for hedging and vague descriptors (NLPGens). We use NLPGens to analyse the use of generalisations in NLP papers across multiple venues and subdomains, and to examine associations with citation counts, hedging, and vague descriptors.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.14770 — 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 2026-10-15T22:29:03.728Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).