eacl2026-assessing-paper-novelty

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.

Identifier
Source https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4988
Metadata Access https://tudatalib.ulb.tu-darmstadt.de/server/oai/openairedata?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:tudatalib.ulb.tu-darmstadt.de:tudatalib/4988
Provenance
Creator Mohammed Afzal, Osama; Nakov, Preslav; Hope, Tom; Gurevych, Iryna
Publisher Technische Universität Darmstadt
Contributor Technische Universität Darmstadt
Publication Year 2026
Rights Creative Commons Attribution-NonCommercial 4.0 International; info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by-nc/4.0
OpenAccess true
Contact https://tudatalib.ulb.tu-darmstadt.de/docs/en/kontakt/
Representation
Language English
Resource Type Text
Format application/zip
Size 209.42 MB
Version 1.0
Discipline Other