34 citations · 212 across the 70 of their papers we have counts for
13 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…
Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information
Lukas Struppek, Dominik Hintersdorf, Hannah Struppek +2
Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference lat…
Chronoberg: A 250-Year Diachronic Corpus with Temporal Affective Lexicons for Language Model Analysis
Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey +5
Large language models (LLMs) excel at operating at scale by leveraging social media and data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long…
ART: Adaptive Relation Tuning for Generalized Relation Prediction
Gopika Sudhakaran, Hikaru Shindo, Patrick Schramowski +3
Visual relation detection (VRD) is the task of identifying the relationships between objects in a scene. VRD models trained solely on relation detection data struggle to generalize…
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…
Finding DoRI: Discovery of Retained Images in Diffusion Models
Antoni Kowalczuk, Dominik Hintersdorf, Lukas Struppek +3
Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectual property remain due to their potent…
How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions
Manuel Brack, Sudeep Katakol, Felix Friedrich +4
Training data is at the core of any successful text-to-image models. The quality and descriptiveness of image text are crucial to a model's performance. Given the noisiness and inc…
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…
SLR: Automated Synthesis for Scalable Logical Reasoning
Lukas Helff, Ahmad Omar, Felix Friedrich +7
We introduce SLR, an end-to-end framework for systematic evaluation and training of Large Language Models (LLMs) via Scalable Logical Reasoning. Given a user's task specification,…
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…
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models
Mehdi Ali, Manuel Brack, Max Lübbering +15
High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets r…
EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition
Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby +7
Effective human-AI interaction relies on AI's ability to accurately perceive and interpret human emotions. Current benchmarks for vision and vision-language models are severely lim…
The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation
Martin Mundt, Anaelia Ovalle, Felix Friedrich +5
In a widely popular analogy by Turing Award Laureate Yann LeCun, machine intelligence has been compared to cake - where unsupervised learning forms the base, supervised learning ad…