SHAP-based explanation methods: a review for NLP interpretability E Mosca, F Szigeti, S Tragianni, D Gallagher, G Groh Proceedings of the 29th international conference on computational …, 2022 | 52 | 2022 |
Understanding and interpreting the impact of user context in hate speech detection E Mosca, M Wich, G Groh Proceedings of the Ninth International Workshop on Natural Language …, 2021 | 39 | 2021 |
" That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks E Mosca, S Agarwal, J Rando-Ramirez, G Groh Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 22 | 2022 |
Accurate cost estimation of memory systems utilizing machine learning and solutions from computer vision for design automation L Servadei, E Mosca, E Zennaro, K Devarajegowda, M Werner, W Ecker, ... IEEE Transactions on Computers 69 (6), 856-867, 2020 | 16 | 2020 |
Detecting word-level adversarial text attacks via SHapley additive exPlanations L Huber, MA Kühn, E Mosca, G Groh Proceedings of the 7th Workshop on Representation Learning for NLP, 156-166, 2022 | 10 | 2022 |
Explainable Abusive Language Classification Leveraging User and Network Data M Wich, E Mosca, A Gorniak, J Hingerl, G Groh The European Conference on Machine Learning and Principles and Practice of …, 2021 | 10 | 2021 |
Distinguishing Fact from Fiction: A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era. E Mosca, MHI Abdalla, P Basso, M Musumeci, G Groh Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing …, 2023 | 9 | 2023 |
A Benchmark Dataset to Distinguish Human-Written and Machine-Generated Scientific Papers MHI Abdalla, S Malberg, D Dementieva, E Mosca, G Groh Information 14 (10), 522, 2023 | 6 | 2023 |
Uncovering trauma in genocide tribunals: An NLP approach using the Genocide Transcript Corpus M Schirmer, IMO Nolasco, E Mosca, S Xu, J Pfeffer Proceedings of the Nineteenth International Conference on Artificial …, 2023 | 6 | 2023 |
GrammarSHAP: An efficient model-agnostic and structure-aware NLP explainer E Mosca, D Demirtürk, L Mülln, F Raffagnato, G Groh Proceedings of the First Workshop on Learning with Natural Language …, 2022 | 5 | 2022 |
Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers E Mosca, K Harmann, T Eder, G Groh Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing …, 2022 | 4 | 2022 |
Explainability of Hate Speech Detection Models E Mosca Technical University of Munich. https://soc.cit.tum.de/persons/edoardo-mosca …, 2020 | 4 | 2020 |
“Dr LLM, what do I have?”: The Impact of User Beliefs and Prompt Formulation on Health Diagnoses W Kusa, E Mosca, A Lipani Proceedings of the Third Workshop on NLP for Medical Conversations, 13-19, 2023 | 2 | 2023 |
IFAN: An explainability-focused interaction framework for humans and NLP models E Mosca, D Dementieva, TE Ajdari, M Kummeth, K Gringauz, Y Zhou, ... arXiv preprint arXiv:2303.03124, 2023 | 2 | 2023 |
Combining evolutionary algorithms and deep learning for hardware/software interface optimization L Servadei, E Mosca, M Werner, V Esen, R Wille, W Ecker 2019 ACM/IEEE 1st Workshop on Machine Learning for CAD (MLCAD), 1-6, 2019 | 2 | 2019 |
Cost estimation for configurable model-driven SoC designs using machine learning L Servadei, E Mosca, K Devarajegowda, M Werner, W Ecker, R Wille Proceedings of the 2020 on Great Lakes Symposium on VLSI, 405-410, 2020 | 1 | 2020 |
Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora? M Anschütz, E Mosca, G Groh arXiv preprint arXiv:2404.06838, 2024 | | 2024 |
A Scenario-Based Approach to the Design and Use of Ethical AI Models in Managing a Health Pandemic G Groh, D Brand, J van der Merwe, ME Hoffmann, T Eder, E Mosca, ... | | 2021 |
Accurate Cost Estimation of Memory Systems L Servadei, E Mosca, E Zennaro, K Devarajegowda, M Werner | | |