Papers by Masha Fedzechkina
Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have shown that multilingual models encode languagespecific information and language-agnostic features, but the nature and interaction of these representations is not fully understood. |
| Approach: | They propose a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning). |
| Outcome: | The proposed tasks show that language discrimination declines over training and strengthens over time and stabilizes in deeper layers. |
Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models (2025.acl-long)
Copied to clipboard
Anirudh Sundar, Sinead Williamson, Katherine Metcalf, Barry-John Theobald, Skyler Seto, Masha Fedzechkina
| Challenge: | Large language models (LLMs) exhibit impressive performance on a variety of tasks from text summarization to zero-shot common-sense reasoning. |
| Approach: | They propose to manipulate the embedding space of mLLMs by manipulating its activations to steer generation into the desired direction. |
| Outcome: | The proposed model interventions improves alignment of cross-lingual representations in multilingual large language models with up to 2x improvements in top-1 accuracy on cross-linguistic retrieval tasks. |
Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter (2025.findings-acl)
Copied to clipboard
| Challenge: | Prior work on cross-lingual transfer often focuses on a small set of languages from a few language families and/or a single task. |
| Approach: | They analyze cross-lingual transfer for 263 languages from a wide variety of language families . they include three popular NLP tasks: POS tagging, dependency parsing, topic classification . |
| Outcome: | The proposed approach is based on linguistic similarity measures for 263 languages . the results show that the effect of linguistic similarities on transfer performance depends on a range of factors . |