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Masha Fedzechkina

7 accepted papers

2026

DSO: Direct Steering Optimization for Bias Mitigation

CVPR 2026

Generative models are often deployed to make decisions on behalf of users, such as vision-language models (VLMs) identifying which person in a room is a doctor to help visually impaired individuals. Yet, VLM decisions are influenced by the perceived demographic attributes of people in the input, whi

Cited by 0SourceScholar
2025

Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter

ACL 2025finding

Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include is unclear. Prior research often focuses on a small set of languages from a few language families and…

2025

Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks

EMNLP 2025

We introduce a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning). Inspired from speech processing, these zero-shot tasks measure whether minimal differences in representation can be relia

Cited by 0SourcePDFScholar
2025

Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models

ACL 2025long

Aligned representations across languages is a desired property in multilingual large language models (mLLMs), as alignment can improve performance in cross-lingual tasks. Typically alignment requires fine-tuning a model, which is computationally expensive, and sizable language data, which often may…

Cited by 0SourcePDFScholar
2024

Can You Rely on Synthetic Labellers in Preference-Based Reinforcement Learning? It’s Complicated

AAAI 2024technical

Preference-based Reinforcement Learning (PbRL) enables non-experts to train Reinforcement Learning models using preference feedback. However, the effort required to collect preference labels from real humans means that PbRL research primarily relies on synthetic labellers. We validate the most commo…

Cited by 2SourcePDFScholar
2023

Naturalistic Head Motion Generation from Speech

ICASSP 2023accepted

Synthesizing natural head motion to accompany speech for an embodied conversational agent is necessary for pro-viding a rich interactive experience. Most prior works assess the quality of generated head motion by comparing them against a single ground-truth using an objective metric. Yet there are m…

Cited by 0SourceScholar
2023

On the Role of LIP Articulation in Visual Speech Perception

ICASSP 2023accepted

Generating realistic lip motion from audio to simulate speech production is critical for driving natural character animation. Previous research has shown that traditional metrics used to optimize and assess models for generating lip motion from speech are not a good indicator of subjective opinion o…

Cited by 0SourceScholar