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Tomasz Limisiewicz

18 papers · 2020–2025 · 8 conferences · across top CS/AI conferences

Achievements

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+8 more ↓ 🏃 Academic Marathon (5) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (8) 🐝 Cross-Pollinator (12)
🏃 Academic Marathon (5) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (12) 🏆 Keyword Champion (2) 🔥 Unstoppable (6) 💎 Century Club (18) Prolific Year (5) 🗃️ Keyword Collector (77)

Conferences

ACL (4) EMNLP (4) NAACL (3) EACL (2) IJCNLP (2) AACL (1) ICLR (1) NIPS (1)

Research topics

Papers

Beyond Literal Token Overlap: Token Alignability for Multilinguality NAACL 2025 MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization NIPS 2024 MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling ACL 2024 Teaching LLMs at Charles University: Assignments and Activities ACL 2024 Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models EMNLP 2024 Debiasing Algorithm through Model Adaptation ICLR 2024 You Can Have Your Data and Balance It Too: Towards Balanced and Efficient Multilingual Models EACL 2023 ÚFAL Submission for SIGTYP Supervised Cognate Detection Task EACL 2023 Exploring the Impact of Training Data Distribution and Subword Tokenization on Gender Bias in Machine Translation AACL 2023 Tokenization Impacts Multilingual Language Modeling: Assessing Vocabulary Allocation and Overlap Across Languages ACL 2023 Exploring the Impact of Training Data Distribution and Subword Tokenization on Gender Bias in Machine Translation IJCNLP 2023 A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank NAACL 2022 Don’t Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information NAACL 2022 Introducing Orthogonal Constraint in Structural Probes ACL 2021 Introducing Orthogonal Constraint in Structural Probes IJCNLP 2021 Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes EMNLP 2021 Gender Coreference and Bias Evaluation at WMT 2020 EMNLP 2020 Universal Dependencies According to BERT: Both More Specific and More General EMNLP 2020