Search Results for author:
Found 7 papers, 3 papers with code
Tower: An Open Multilingual Large Language Model for Translation-Related Tasks
While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only when specializing on a single task.
Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning
Experiments on 10 language pairs show that our proposed approach recovers the original few-shot capabilities while keeping the added benefits of finetuning.
Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task
Our team participated on all tasks: sentence- and word-level quality prediction (task 1) and fine-grained error span detection (task 2).
Fairness-Aware Data Valuation for Supervised Learning
Data valuation is a ML field that studies the value of training instances towards a given predictive task.
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset.
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within.
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within.
