34 citations · 137 across the 37 of their papers we have counts for
12 papers · 1 filter
LIME: Making LLM Data More Efficient with Linguistic Metadata Embeddings
Sebastian Sztwiertnia, Felix Friedrich, Kristian Kersting +2
Pre-training decoder-only language models relies on vast amounts of high-quality data, yet the availability of such data is increasingly reaching its limits. While metadata is comm…
Beyond Overcorrection: Evaluating Diversity in T2I Models with DivBench
Felix Friedrich, Thiemo Ganesha Welsch, Manuel Brack +2
Current diversification strategies for text-to-image (T2I) models often ignore contextual appropriateness, leading to over-diversification where demographic attributes are modified…
Measuring and Guiding Monosemanticity
Ruben Härle, Felix Friedrich, Manuel Brack +4
There is growing interest in leveraging mechanistic interpretability and controllability to better understand and influence the internal dynamics of large language models (LLMs). H…
EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection
Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby +6
Speech emotion recognition (SER) systems are constrained by existing datasets that typically cover only 6-10 basic emotions, lack scale and diversity, and face ethical challenges w…
MSTS: A Multimodal Safety Test Suite for Vision-Language Models
Paul Röttger, Giuseppe Attanasio, Felix Friedrich +19
Vision-language models (VLMs), which process image and text inputs, are increasingly integrated into chat assistants and other consumer AI applications. Without proper safeguards,…
SCAR: Sparse Conditioned Autoencoders for Concept Detection and Steering in LLMs
Ruben Härle, Felix Friedrich, Manuel Brack +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like text, but their output may not be aligned with the user or even produce harmful cont…
LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Inconsistencies
Felix Friedrich, Simone Tedeschi, Patrick Schramowski +5
Building safe Large Language Models (LLMs) across multiple languages is essential in ensuring both safe access and linguistic diversity. To this end, we conduct a large-scale, comp…
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming
Simone Tedeschi, Felix Friedrich, Patrick Schramowski +4
When building Large Language Models (LLMs), it is paramount to bear safety in mind and protect them with guardrails. Indeed, LLMs should never generate content promoting or normali…
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Taishi Nakamura, Mayank Mishra, Simone Tedeschi +42
Pretrained language models are an integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim…
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You
Felix Friedrich, Katharina Hämmerl, Patrick Schramowski +4
Text-to-image generation models have recently achieved astonishing results in image quality, flexibility, and text alignment, and are consequently employed in a fast-growing number…
Revision Transformers: Instructing Language Models to Change their Values
Felix Friedrich, Wolfgang Stammer, Patrick Schramowski +1
Current transformer language models (LM) are large-scale models with billions of parameters. They have been shown to provide high performances on a variety of tasks but are also pr…
Interactively Providing Explanations for Transformer Language Models
Felix Friedrich, Patrick Schramowski, Christopher Tauchmann +1
Transformer language models are state of the art in a multitude of NLP tasks. Despite these successes, their opaqueness remains problematic. Recent methods aiming to provide interp…