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2020 – today
- 2025
[c13]Tomer Ashuach, Martin Tutek, Yonatan Belinkov:
REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space. ACL (Findings) 2025: 14774-14797- [i11]Martin Tutek, Fateme Hashemi Chaleshtori, Ana Marasovic, Yonatan Belinkov:
Measuring Faithfulness of Chains of Thought by Unlearning Reasoning Steps. CoRR abs/2502.14829 (2025) - [i10]Aaron Mueller, Atticus Geiger, Sarah Wiegreffe, Dana Arad, Iván Arcuschin, Adam Belfki, Yik Siu Chan, Jaden Fiotto-Kaufman, Tal Haklay, Michael Hanna, Jing Huang, Rohan Gupta, Yaniv Nikankin, Hadas Orgad, Nikhil Prakash, Anja Reusch, Aruna Sankaranarayanan, Shun Shao, Alessandro Stolfo, Martin Tutek, Amir Zur, David Bau, Yonatan Belinkov:
MIB: A Mechanistic Interpretability Benchmark. CoRR abs/2504.13151 (2025) - [i9]David Dukic, Ana Baric, Marko Culjak, Josip Jukic, Martin Tutek:
Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings. CoRR abs/2506.13569 (2025) - [i8]Tomer Ashuach, Dana Arad, Aaron Mueller, Martin Tutek, Yonatan Belinkov:
CRISP: Persistent Concept Unlearning via Sparse Autoencoders. CoRR abs/2508.13650 (2025) - 2024
- [c12]Rachneet Sachdeva, Martin Tutek, Iryna Gurevych:
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration. EACL (1) 2024: 1876-1898 - [c11]Haritz Puerto, Martin Tutek, Somak Aditya, Xiaodan Zhu, Iryna Gurevych:
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs. EMNLP 2024: 11234-11258 - [c10]Fran Jelenic, Josip Jukic, Martin Tutek, Mate Puljiz, Jan Snajder:
Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness. ICLR 2024 - [i7]Haritz Puerto, Martin Tutek, Somak Aditya
, Xiaodan Zhu, Iryna Gurevych:
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs. CoRR abs/2401.10065 (2024) - [i6]Tomer Ashuach, Martin Tutek, Yonatan Belinkov:
REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space. CoRR abs/2406.09325 (2024) - 2023
- [c9]Josip Jukic, Martin Tutek, Jan Snajder:
Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency Methods. ACL (Findings) 2023: 9147-9162 - [i5]Rachneet Sachdeva, Martin Tutek, Iryna Gurevych:
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration. CoRR abs/2309.07822 (2023) - [i4]Fran Jelenic, Josip Jukic, Martin Tutek, Mate Puljiz, Jan Snajder:
Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness. CoRR abs/2310.02832 (2023) - 2022
- [b1]Martin Tutek:
Extending the recurrent neural network model for improved compositional modelling of text sequences ; Proširenje modela povratnih neuronskih mreža za poboljšano kompozicijsko modeliranje tekstnih sljedova. University of Zagreb, Croatia, 2022 - [j1]Martin Tutek, Jan Snajder:
Toward Practical Usage of the Attention Mechanism as a Tool for Interpretability. IEEE Access 10: 47011-47030 (2022) - [c8]Leo Obadic, Martin Tutek, Jan Snajder:
NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter. WASSA@ACL 2022: 286-292 - [i3]Josip Jukic, Martin Tutek, Jan Snajder:
Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency Methods. CoRR abs/2211.08369 (2022) - 2020
- [c7]Martin Tutek, Jan Snajder:
Staying True to Your Word: (How) Can Attention Become Explanation? RepL4NLP@ACL 2020: 131-142 - [i2]Martin Tutek, Jan Snajder:
Staying True to Your Word: (How) Can Attention Become Explanation? CoRR abs/2005.09379 (2020)
2010 – 2019
- 2018
- [c6]Martin Tutek, Jan Snajder:
Iterative Recursive Attention Model for Interpretable Sequence Classification. BlackboxNLP@EMNLP 2018: 249-257 - [i1]Martin Tutek, Jan Snajder:
Iterative Recursive Attention Model for Interpretable Sequence Classification. CoRR abs/1808.10503 (2018) - 2017
- [c5]Maria Pia di Buono, Martin Tutek, Jan Snajder, Goran Glavas, Bojana Dalbelo Basic, Natasa Milic-Frayling:
Two Layers of Annotation for Representing Event Mentions in News Stories. LAW@ACL 2017: 82-90 - [c4]Maria Pia di Buono, Jan Snajder, Bojana Dalbelo Basic, Goran Glavas, Martin Tutek, Natasa Milic-Frayling:
Predicting News Values from Headline Text and Emotions. NLPmJ@EMNLP 2017: 1-6 - [c3]Leon Rotim, Martin Tutek, Jan Snajder:
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news. SemEval@ACL 2017: 866-871 - 2016
- [c2]Martin Tutek, Goran Glavas, Jan Snajder, Natasa Milic-Frayling, Bojana Dalbelo Basic:
Detecting and Ranking Conceptual Links between Texts Using a Knowledge Base. CIKM 2016: 2077-2080 - [c1]Martin Tutek, Ivan Sekulic, Paula Gombar, Ivan Paljak, Filip Culinovic, Filip Boltuzic, Mladen Karan, Domagoj Alagic, Jan Snajder:
TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble. SemEval@NAACL-HLT 2016: 464-468
Coauthor Index
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last updated on 2025-09-27 03:15 CEST by the dblp team
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