eacl2026-assessing-paper-novelty
| dc.contributor.author | Mohammed Afzal, Osama | |
| dc.contributor.author | Nakov, Preslav | |
| dc.contributor.author | Hope, Tom | |
| dc.contributor.author | Gurevych, Iryna | |
| dc.date.accessioned | 2026-01-16T11:34:26Z | |
| dc.date.created | 2026-01-16 | |
| dc.date.issued | 2026-01-16 | |
| dc.description | Dataset for evaluating automated novelty assessment in academic papers. Contains 182 ICLR submissions with human annotations, LLM-derived novelty assessments from reviewer critiques, and system-generated novelty analyses including research landscape overviews and novelty delta comparisons with prior work. This dataset is a supplement to the paper: Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback. Please refer to the paper for more details. | |
| dc.description.version | 1.0 | |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4988 | |
| dc.language.iso | en | |
| dc.rights.license | CC-BY-NC-4.0 (https://creativecommons.org/licenses/by-nc/4.0) | |
| dc.subject | Peer Review | |
| dc.subject | AI for Science | |
| dc.subject.classification | 4.43-04 | |
| dc.subject.ddc | 004 | |
| dc.title | eacl2026-assessing-paper-novelty | |
| dc.type | Text | |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| tuda.agreements | true | |
| tuda.unit | TUDa |
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| Name | Description | Size | Format | |
|---|---|---|---|---|
| data_novelty_assessment.zip | 209.42 MB | ZIP-Archivdateien |