aiqbench analysis
This article introduces Loci Similes, a new benchmark designed to evaluate language models on detecting intertextual connections in Latin literature. By addressing the scarcity of standardized datasets for classical texts, it establishes baselines for using pretrained encoder models to identify semantic similarities beyond simple lexical overlap. The work focuses specifically on advancing machine learning capabilities within the domain of digital humanities and historical text analysis.
Loci Similes: A Benchmark for Extracting Intertextualities in Latin Literature
arXiv:2601.07533v3 Announce Type: replace-cross Abstract: Tracing connections between historical texts is an important part of intertextual research, enabling scholars to reconstruct the virtual library of a writer and identify the sources influencing their creative process. These intertextual links manifest in diverse forms, ranging from direct verbatim quotations to subtle allusions and paraphrases disguised by morphological variation. Language models offer a promising path forward due to their capability of capturing semantic similarity beyond lexical overlap. However, the development of new methods for this task is held back by the scarcity of standardized benchmarks and easy-to-use datasets. We address this gap by introducing Loci Similes, a benchmark for Latin intertextuality detection comprising a curated dataset of ~176k text segments and 1,490 expert-verified parallels, including 945 labeled references from an existing dataset. Using this data, we establish baselines for retrieval and classification of intertextualities with both lexical methods and pretrained encoder language models.
Abstract:Tracing connections between historical texts is an important part of intertextual research, enabling scholars to reconstruct the virtual library of a writer and identify the sources influencing their creative process. These intertextual links manifest in diverse forms, ranging from direct verbatim quotations to subtle allusions and paraphrases disguised by morphological variation. Language models offer a promising path forward due to their capability of capturing semantic similarity beyond lexical overlap. However, the development of new methods for this task is held back by the scarcity of standardized benchmarks and easy-to-use datasets. We address this gap by introducing Loci Similes, a benchmark for Latin intertextuality detection comprising a curated dataset of ~176k text segments and 1,490 expert-verified parallels, including 945 labeled references from an existing dataset. Using this data, we establish baselines for retrieval and classification of intertextualities with both lexical methods and pretrained encoder language models.
Information Retrieval (cs.IR); Computation and Language (cs.CL); Digital Libraries (cs.DL)
arXiv:2601.07533 [cs.IR]
(or arXiv:2601.07533v3 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2601.07533 Focus to learn more arXiv-issued DOI via DataCite
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