6 citations · 9 across the 6 of their papers we have counts for
4 papers · 1 filter
From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense Consistency
Xenia Ohmer, Elia Bruni, Dieuwke Hupkes
The staggering pace with which the capabilities of large language models (LLMs) are increasing, as measured by a range of commonly used natural language understanding (NLU) benchma…
GRASP: A novel benchmark for evaluating language GRounding And Situated Physics understanding in multimodal language models
Serwan Jassim, Mario Holubar, Annika Richter +3
This paper presents GRASP, a novel benchmark to evaluate the language grounding and physical understanding capabilities of video-based multimodal large language models (LLMs). This…
On the Relationship between Skill Neurons and Robustness in Prompt Tuning
Leon Ackermann, Xenia Ohmer
Prompt Tuning is a popular parameter-efficient finetuning method for pre-trained large language models (PLMs). Based on experiments with RoBERTa, it has been suggested that Prompt…
Separating form and meaning: Using self-consistency to quantify task understanding across multiple senses
Xenia Ohmer, Elia Bruni, Dieuwke Hupkes
At the staggering pace with which the capabilities of large language models (LLMs) are increasing, creating future-proof evaluation sets to assess their understanding becomes more…