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New Greek book retrieval benchmark CUP released

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Researchers have introduced CUP, a new benchmark dataset designed for evaluating Greek book retrieval systems. The dataset comprises 868 catalog records and 104 expert-annotated queries with relevance judgments. Experiments show that multilingual embeddings outperform Greek-specific models, and hybrid retrieval methods achieve the best overall performance. Analysis indicates BM25 is effective for named-entity queries, while dense and hybrid approaches improve performance on natural-language, noisy, cross-lingual, and concept-based queries. AI

IMPACT Provides a benchmark for evaluating LLM and embedding-based retrieval systems in a specific domain.

RANK_REASON The item is an academic paper detailing a new dataset and evaluation of retrieval methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New Greek book retrieval benchmark CUP released

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The item is an academic paper detailing a new dataset and evaluation of retrieval methods. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Katerina Papantoniou, Panagiotis Papadakos, Theodore Patkos, Dimitris Garefalakis, Nikos Vardakis, Dimitris Plexousakis ·

    A Comparative Evaluation of Embeddings and LLMs in a Greek Book Publisher Setting - The CUP Dataset

    arXiv:2607.21274v1 Announce Type: cross Abstract: We present CUP, a Greek book retrieval benchmark consisting of 868 catalog records and 104 expert-annotated queries with graded relevance judgments. We evaluate sparse (BM25), dense (sentence-transformers), hybrid, and LLM-assiste…

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