2 citations · 3 across the 5 of their papers we have counts for
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MILE-RefHumEval: A Reference-Free, Multi-Independent LLM Framework for Human-Aligned Evaluation
Nalin Srun, Parisa Rastin, Guénaël Cabanes +1
We introduce MILE-RefHumEval, a reference-free framework for evaluating Large Language Models (LLMs) without ground-truth annotations or evaluator coordination. It leverages an ens…
Multilingual Clinical NER: Translation or Cross-lingual Transfer?
Xavier Fontaine, Félix Gaschi, Parisa Rastin +1
Natural language tasks like Named Entity Recognition (NER) in the clinical domain on non-English texts can be very time-consuming and expensive due to the lack of annotated data. C…
Exploring the Relationship between Alignment and Cross-lingual Transfer in Multilingual Transformers
Félix Gaschi, Patricio Cerda, Parisa Rastin +1
Without any explicit cross-lingual training data, multilingual language models can achieve cross-lingual transfer. One common way to improve this transfer is to perform realignment…
Multilingual Transformer Encoders: a Word-Level Task-Agnostic Evaluation
Félix Gaschi, François Plesse, Parisa Rastin +1
Some Transformer-based models can perform cross-lingual transfer learning: those models can be trained on a specific task in one language and give relatively good results on the sa…