4 citations · 7 across the 10 of their papers we have counts for
10 papers
Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions
Anastasiia Sedova, Natalie Schluter, Skyler Seto +1
Cross-lingual knowledge transfer is critical for building high-performing multilingual language models for languages with insufficient training data. When target language data is s…
Scaling Laws for Mixture Pretraining Under Data Constraints
Anastasiia Sedova, Skyler Seto, Natalie Schluter +1
As language models scale, the amount of data they require grows -- yet many target data sources, such as low-resource languages or specialized domains, are inherently limited in si…
Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings
Paul Jeha, Anastasiia Sedova, Louis Béthune +4
For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many times, degrading generalization…
To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity
Anastasiia Sedova, Robert Litschko, Diego Frassinelli +2
One of the major aspects contributing to the striking performance of large language models (LLMs) is the vast amount of factual knowledge accumulated during pre-training. Yet, many…
Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency
Vasiliki Kougia, Anastasiia Sedova, Andreas Stephan +2
This paper presents the first study for temporal relation extraction in a zero-shot setting focusing on biomedical text. We employ two types of prompts and five LLMs (GPT-3.5, Mixt…
Exploring prompts to elicit memorization in masked language model-based named entity recognition
Yuxi Xia, Anastasiia Sedova, Pedro Henrique Luz de Araujo +3
Training data memorization in language models impacts model capability (generalization) and safety (privacy risk). This paper focuses on analyzing prompts' impact on detecting the…
Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal
Anastasiia Sedova, Lena Zellinger, Benjamin Roth
An accurate and substantial dataset is essential for training a reliable and well-performing model. However, even manually annotated datasets contain label errors, not to mention a…
ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion
Anastasiia Sedova, Benjamin Roth
Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph.…
ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision
Anastasiia Sedova, Benjamin Roth
A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), ru…
Knodle: Modular Weakly Supervised Learning with PyTorch
Anastasiia Sedova, Andreas Stephan, Marina Speranskaya +1
Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a…