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Martin Tutek

TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble
2016 · Martin Tutek, Ivan Sekulic, et al. · International Workshop on Semantic Evaluation (SemEval) · 50
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Toward Practical Usage of the Attention Mechanism as a Tool for Interpretability
2022 · Martin Tutek, Jan Snajder · IEEE Access · 30
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Staying True to Your Word: (How) Can Attention Become Explanation?
2020 · Martin Tutek, Jan Snajder · Workshop on Representation Learning for NLP (RepL4NLP) · 27
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TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
2017 · Leon Rotim, Martin Tutek, et al. · International Workshop on Semantic Evaluation (SemEval) · 11
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Iterative Recursive Attention Model for Interpretable Sequence Classification
2018 · Martin Tutek, Jan Šnajder · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · 10
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Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs
2024 · Haritz Puerto, Martin Tutek, et al. · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 6
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Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps
2025 · Martin Tutek, Fateme Hashemi Chaleshtori, et al. · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 4
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Predicting News Values from Headline Text and Emotions
2017 · Maria Pia di Buono, Jan Šnajder, et al. · 4
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Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness
2023 · Jelenić, Fran, Jukić, Josip, et al. · arXiv (Cornell University) · 2
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NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter
2022 · Leo Obadić, Martin Tutek, et al. · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · 2
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Detecting and Ranking Conceptual Links between Texts Using a Knowledge Base
2016 · Martin Tutek, Goran Glavas, et al. · ACM International Conference on Information and Knowledge Management (CIKM) · 2
Reasoning Models Know What's Important, and Encode It in Their Activations
2026 · Nikankin, Yaniv, Tutek, Martin, et al. · arXiv (Cornell University) · 1
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PRAGWORLD: A Benchmark Evaluating LLMs’ Local World Model Under Minimal Linguistic Alterations and Conversational Dynamics
2026 · Sachin Vashistha, Aryan Bibhuti, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 1
MIB: A Mechanistic Interpretability Benchmark
2025 · Mueller, Aaron, Geiger, Atticus, et al. · UvA-DARE (University of Amsterdam) · 1
REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space
2025 · Tomer Ashuach, Martin Tutek, et al. · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · 1
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Findings of the BlackboxNLP 2025 Shared Task: Localizing Circuits and Causal Variables in Language Models
2025 · Dana Arad, Yonatan Belinkov, et al. · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · 1
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CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration
2024 · Rachneet Sachdeva, Martin Tutek, et al. · Conference of the European Chapter of the Association for Computational Linguistics · 1
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Two Layers of Annotation for Representing Event Mentions in News Stories
2017 · Maria Pia di Buono, Martin Tutek, et al. · Linguistic Annotation Workshop · 1
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Evaluating Pluralism in LLMs through Latent Perspectives
2026 · Majer, Laura, Šnajder, Jan, et al. · arXiv (Cornell University)
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CRISP: Persistent Concept Unlearning via Sparse Autoencoders
2026 · Association for Computational Linguistics 2026, Arad, Dana, et al. · Underline Science Inc.
Sequence Repetition Enhances Token Embeddings and Improves Sequence Labeling with Decoder-only Language Models
2026 · Association for Computational Linguistics 2026, Čuljak, Marko, et al. · Underline Science Inc.
Old Habits Die Hard: How Conversational History Geometrically Traps LLMs
2026 · Simhi, Adi, Barez, Fazl, et al. · arXiv (Cornell University)
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Sequence Repetition Enhances Token Embeddings and Improves Sequence Labeling with Decoder-only Language Models
2026 · Kukić, Matija Luka, Čuljak, Marko, et al. · arXiv (Cornell University)
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PRAGWORLD: A Benchmark Evaluating LLMs’ Local World Model Under Minimal Linguistic Alterations and Conversational Dynamics
2026 · Association for Artificial Intelligence 2026, Aditya, Somak, et al. · Underline Science Inc.
CRISP: Persistent Concept Unlearning via Sparse Autoencoders
2026 · Tomer Ashuach, Dana Arad, et al. · Proceedings of the conference - Association for Computational Linguistics. Meeting
Works count: 37
Citation count: 155
H-index: 6
I10-index: 5
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