55 citations · 101 across the 10 of their papers we have counts for
10 papers
Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation
Thomas F Burns, Letitia Parcalabescu, Stephan Wäldchen +5
Scaling data quantity is essential for large language models (LLMs), yet recent findings show that data quality can significantly boost performance and training efficiency. We intr…
Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?
Letitia Parcalabescu, Anette Frank
Vision and language model (VLM) decoders are currently the best-performing architectures on multimodal tasks. Next to answers, they are able to produce natural language explanation…
ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
Ilker Kesen, Andrea Pedrotti, Mustafa Dogan +8
With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their v…
On Measuring Faithfulness or Self-consistency of Natural Language Explanations
Letitia Parcalabescu, Anette Frank
Large language models (LLMs) can explain their predictions through post-hoc or Chain-of-Thought (CoT) explanations. But an LLM could make up reasonably sounding explanations that a…
MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks
Letitia Parcalabescu, Anette Frank
Vision and language models (VL) are known to exploit unrobust indicators in individual modalities (e.g., introduced by distributional biases) instead of focusing on relevant inform…
VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena
Letitia Parcalabescu, Michele Cafagna, Lilitta Muradjan +3
We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V&L) models for their visio-lin…
MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning
Constantin Eichenberg, Sidney Black, Samuel Weinbach +2
Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of…
What is Multimodality?
Letitia Parcalabescu, Nils Trost, Anette Frank
The last years have shown rapid developments in the field of multimodal machine learning, combining e.g., vision, text or speech. In this position paper we explain how the field us…
Seeing past words: Testing the cross-modal capabilities of pretrained V&L models on counting tasks
Letitia Parcalabescu, Albert Gatt, Anette Frank +1
We investigate the reasoning ability of pretrained vision and language (V&L) models in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pa…
AMR Similarity Metrics from Principles
Juri Opitz, Letitia Parcalabescu, Anette Frank
Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs…