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Found 5 papers, 3 papers with code
Where does In-context Translation Happen in Large Language Models
Self-supervised large language models have demonstrated the ability to perform Machine Translation (MT) via in-context learning, but little is known about where the model performs the task with respect to prompt instructions and demonstration examples.
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?
Learning these models often requires specialized training algorithms that address task-conflict in the shared parameter updates, which otherwise can lead to negative transfer.
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three
However, extending these methods to structured prediction is not always straightforward or effective; furthermore, a held-out calibration set may not always be available.
Sources of Transfer in Multilingual Named Entity Recognition
However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Effective Use of Context in Noisy Entity Linking
To disambiguate between closely related concepts, entity linking systems need to effectively distill cues from their context, which may be quite noisy.
