activity
20212026
most citedLearning with Noisy Labels by Adaptive Gradient-Based Outlier Removal

1 citations · 3 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CL2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.CL2024

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…

cs.CL2024★ 1 cited

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…

cs.CL2024

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…