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Aitor Soroa

13 accepted papers

2025

Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque

Oscar Sainz, Naiara Perez, Julen Etxaniz, Joseba Fernandez de Landa, Itziar Aldabe, Iker Garc{\'i}a-Ferrero +6
EMNLP 2025

Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to conventional instruction adaptation pipelines in low-resource scenarios. We assume a realistic scenario for low-resourc

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

A LLM-based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation

Irune Zubiaga, Aitor Soroa, Rodrigo Agerri
EMNLP 2024finding

This paper proposes a novel approach to evaluate Counter Narrative (CN) generation using a Large Language Model (LLM) as an evaluator. We show that traditional automatic metrics correlate poorly with human judgements and fail to capture the nuanced relationship between generated CNs and human percep…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2024

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

Julen Etxaniz, Gorka Azkune, Aitor Soroa, Oier Lopez de Lacalle, Mikel Artetxe
NeurIPS 2024poster

Large Language Models (LLMs) exhibit extensive knowledge about the world, but most evaluations have been limited to global or anglocentric subjects. This raises the question of how well these models perform on topics relevant to other cultures, whose presence on the web is not that prominent. To add…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2024

Do Multilingual Language Models Think Better in English?

Julen Etxaniz, Gorka Azkune, Aitor Soroa, Oier Lopez de Lacalle, Mikel Artetxe
NAACL 2024short

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 before running inference. However, these improvements can be attributed to the use of a separate tr…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2024

Latxa: An Open Language Model and Evaluation Suite for Basque

Julen Etxaniz, Oscar Sainz, Naiara Miguel, Itziar Aldabe, German Rigau, Eneko Agirre +3
ACL 2024long

We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters. Latxa is based on Llama 2, which we continue pretraining on a new Basque corpus comprising 4.3M documents and 4.2B tokens. Addressing the scarcity of high-quality benchmarks for Basque, we furth…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2024

XNLIeu: a dataset for cross-lingual NLI in Basque

Maite Heredia, Julen Etxaniz, Muitze Zulaika, Xabier Saralegi, Jeremy Barnes, Aitor Soroa
NAACL 2024long

XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. In this paper, we expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches.…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2023

Scaling Laws for BERT in Low-Resource Settings

Gorka Urbizu, Iñaki San Vicente, Xabier Saralegi, Rodrigo Agerri, Aitor Soroa
ACL 2023findings

Large language models are very resource intensive, both financially and environmentally, and require an amount of training data which is simply unobtainable for the majority of NLP practitioners. Previous work has researched the scaling laws of such models, but optimal ratios of model parameters, da…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2022

Does Corpus Quality Really Matter for Low-Resource Languages?

Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri, Olatz Perez-de-Viñaspre, Aitor Soroa
EMNLP 2022main

The vast majority of non-English corpora are derived from automatically filtered versions of CommonCrawl. While prior work has identified major issues on the quality of these datasets (Kreutzer et al., 2021), it is not clear how this impacts downstream performance. Taking representation learning in…

Cited by 38Source ↗PDF ↗code ↗Scholar ↗
2022

PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation

Aitor Ormazabal, Mikel Artetxe, Manex Agirrezabal, Aitor Soroa, Eneko Agirre
EMNLP 2022finding

Formal verse poetry imposes strict constraints on the meter and rhyme scheme of poems. Most prior work on generating this type of poetry uses existing poems for supervision, which are difficult to obtain for most languages and poetic forms. In this work, we propose an unsupervised approach to genera…

Cited by 26Source ↗PDF ↗code ↗Scholar ↗
2022

Principled Paraphrase Generation with Parallel Corpora

Aitor Ormazabal, Mikel Artetxe, Aitor Soroa, Gorka Labaka, Eneko Agirre
ACL 2022long

Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision. In this paper, we formalize the implicit similarity function induced by this approach, and show that it is susceptible to non-paraphrase pairs sharin…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2022

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao +48
NeurIPS 2022accept

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large lan…

Cited by 214Source ↗PDF ↗Scholar ↗
2021

Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring

Aitor Ormazabal, Mikel Artetxe, Aitor Soroa, Gorka Labaka, Eneko Agirre
ACL 2021long

Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes de…

Cited by 15Source ↗PDF ↗Scholar ↗
2020

Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning

Jon Ander Campos, Kyunghyun Cho, Arantxa Otegi, Aitor Soroa, Eneko Agirre, Gorka Azkune
COLING 2020main

The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility. In most applications, users are not able to provide the correct answer to the system, but they are able to provide binary…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗