most citedNLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

9 citations · 10 across the 5 of their papers we have counts for

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5 papers

cs.CL2024

Data Contamination Report from the 2024 CONDA Shared Task

Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25

The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…

cs.CL2024

Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis

Mikel Zubillaga, Oscar Sainz, Ainara Estarrona +2

Cross-lingual transfer-learning is widely used in Event Extraction for low-resource languages and involves a Multilingual Language Model that is trained in a source language and ap…

cs.CL20239 cited

NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

Oscar Sainz, Jon Ander Campos, Iker García-Ferrero +3

In this position paper, we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble. The worst kind of data contamina…

cs.CL20231 cited

IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases

Iker García-Ferrero, Jon Ander Campos, Oscar Sainz +2

Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like…

cs.CL2023

What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories

Oscar Sainz, Oier Lopez de Lacalle, Eneko Agirre +1

Language Models are the core for almost any Natural Language Processing system nowadays. One of their particularities is their contextualized representations, a game changer featur…