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Oier Lopez

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Articles by Oier Lopez

Computer Science > Computation and Language

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By de Lacalle, Oier Lopez
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Do not be greedy, Think Twice: Sampling and Selection for Document-level Information Extraction

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By de Lacalle, Oier Lopez
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (466) Issue with article?

[2512.08713] Automatic Essay Scoring and Feedback Generation in Basque Language Learning

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By de Lacalle, Oier Lopez
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models

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By Juan Carlos, de Lacalle, Oier Lopez, Alejandro Uribe
| arXiv
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[2503.03854] Vision-Language Models Struggle to Align Entities across Modalities

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By de Lacalle, Oier Lopez
| arXiv
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[2503.01851] Euskarazko lehen C1 ebaluatzaile automatikoa

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By de Lacalle, Oier Lopez
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (390) Issue with article?

BertaQA: How Much Do Language Models Know About Local Culture?

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By de Lacalle, Oier Lopez, Gorka Azkune, Aitor Soroa
| arXiv
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Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis

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By de Lacalle, Oier Lopez
| arXiv
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[2403.00587] Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived Dataset

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By de Lacalle, Oier Lopez
| arXiv
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GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction

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By de Lacalle, Oier Lopez, Iker García-Ferrero, Rodrigo Agerri
| arXiv
arXiv:2310.03668 (cs) [Submitted on 5 Oct 2023 (v1), last revised 6 Mar 2024 (this version, v5)] Download PDF HTML (experimental) Comments: Subjects: Computation and Language (cs.CL) Cite as: arXiv:2310.03668 [cs.CL] (or arXiv:2310.03668v5 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2310.03668 Submission history From: Iker García-Ferrero [ view email] [v1] Thu, 5 Oct 2023 16:43:13 UTC (9,011 KB) [v2] Fri, 6 Oct 2023 17:41:15 UTC (9,011 KB) [v3] Mon, 11 Dec 2023 08:24:40 UTC...
Open in Who Shared View Duplicates (270) Issue with article?

[2308.01223] Do Multilingual Language Models Think Better in English?

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By de Lacalle, Oier Lopez
| arXiv
Translate-test is a popular technique to improve the performance of multilingual language models. This approach works by translating the input into English using an external machine translation system, and running inference over the translated input. However, these improvements can be attributed to the use of a separate translation system, which is typically trained on large amounts of parallel data not seen by the language model.
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