55 citations · 101 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…