Patrick Schramowski
Orcid: 0000-0003-1231-7120
According to our database1,
Patrick Schramowski
authored at least 57 papers
between 2018 and 2024.
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Bibliography
2024
Soft Begging: Modular and Efficient Shielding of LLMs against Prompt Injection and Jailbreaking based on Prompt Tuning.
CoRR, 2024
T-FREE: Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings.
CoRR, 2024
CoRR, 2024
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming.
CoRR, 2024
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You.
CoRR, 2024
Divergent Token Metrics: Measuring degradation to prune away LLM components - and optimize quantization.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024
Exploiting Cultural Biases via Homoglyphs inText-to-Image Synthesis (Abstract Reprint).
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
2023
Nat. Mac. Intell., March, 2023
J. Artif. Intell. Res., 2023
Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge.
CoRR, 2023
Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World's Ugliness?
CoRR, 2023
AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation.
CoRR, 2023
ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023
2022
Large pre-trained language models contain human-like biases of what is right and wrong to do.
Nat. Mach. Intell., 2022
CoRR, 2022
LAION-5B: An open large-scale dataset for training next generation image-text models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the HHAI 2022: Augmenting Human Intellect, 2022
Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022
Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
2021
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021
Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations.
Nat. Mach. Intell., 2020
Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations.
CoRR, 2020
Proceedings of the ICMI '20: International Conference on Multimodal Interaction, 2020
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020
2019
Remote. Sens., 2019
CoRR, 2019
Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019
2018