6 citations · 9 across the 11 of their papers we have counts for
11 papers
Beyond Prompt and Parser Engineering: Soft-Prompt Tuning for Automated LLM Evaluation
Selen Erkan, Bastian Boll, Kristian Kersting +2
Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.g., on the model's ability to follow specific formatting requirements. This esp…
A Family of LLMs Liberated from Static Vocabularies
Aleph Alpha, :, Adnen Abdessaied +35
Tokenization is a central component of natural language processing in current large language models (LLMs), enabling models to convert raw text into processable units. Although lea…
Sigma Flows for Image and Data Labeling and Learning Structured Prediction
Jonas Cassel, Bastian Boll, Stefania Petra +2
This paper introduces the sigma flow model for the prediction of structured labelings of data observed on Riemannian manifolds, including Euclidean image domains as special case. T…
Generative Assignment Flows for Representing and Learning Joint Distributions of Discrete Data
Bastian Boll, Daniel Gonzalez-Alvarado, Stefania Petra +1
We introduce a novel generative model for the representation of joint probability distributions of a possibly large number of discrete random variables. The approach uses measure t…
Generative Modeling of Discrete Joint Distributions by E-Geodesic Flow Matching on Assignment Manifolds
Bastian Boll, Daniel Gonzalez-Alvarado, Christoph Schnörr
This paper introduces a novel generative model for discrete distributions based on continuous normalizing flows on the submanifold of factorizing discrete measures. Integration of…
A Geometric Embedding Approach to Multiple Games and Multiple Populations
Bastian Boll, Jonas Cassel, Peter Albers +2
This paper studies a meta-simplex concept and geometric embedding framework for multi-population replicator dynamics. Central results are two embedding theorems which constitute a…
Quantum State Assignment Flows
Jonathan Schwarz, Jonas Cassel, Bastian Boll +3
This paper introduces assignment flows for density matrices as state spaces for representing and analyzing data associated with vertices of an underlying weighted graph. Determinin…
On Certified Generalization in Structured Prediction
Bastian Boll, Christoph Schnörr
In structured prediction, target objects have rich internal structure which does not factorize into independent components and violates common i.i.d. assumptions. This challenge be…
A Nonlocal Graph-PDE and Higher-Order Geometric Integration for Image Labeling
Dmitrij Sitenko, Bastian Boll, Christoph Schnörr
This paper introduces a novel nonlocal partial difference equation (G-PDE) for labeling metric data on graphs. The G-PDE is derived as nonlocal reparametrization of the assignment…
Self-Certifying Classification by Linearized Deep Assignment
Bastian Boll, Alexander Zeilmann, Stefania Petra +1
We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC-Bayes risk certification paradigm. Classifiers are realized as linearly…
Assignment Flow for Order-Constrained OCT Segmentation
D. Sitenko, B. Boll, C. Schnörr
At the present time Optical Coherence Tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal t…