8 papers
Resource-Lean Lexicon Induction for German Dialects
Robert Litschko, Barbara Plank, Diego Frassinelli
Automatic induction of high-quality dictionaries is essential for building lexical resources, yet low-resource languages and dialects pose several challenges: limited access to ann…
I Came, I Saw, I Explained: Benchmarking Multimodal LLMs on Figurative Meaning in Memes
Shijia Zhou, Saif M. Mohammad, Barbara Plank +1
Internet memes represent a popular form of multimodal online communication and often use figurative elements to convey layered meaning through the combination of text and images. H…
Controlling Reading Ease with Gaze-Guided Text Generation
Andreas Säuberli, Darja Jepifanova, Diego Frassinelli +1
The way our eyes move while reading can tell us about the cognitive effort required to process the text. In the present study, we use this fact to generate texts with controllable…
LeWiDi-2025 at NLPerspectives: Third Edition of the Learning with Disagreements Shared Task
Elisa Leonardelli, Silvia Casola, Siyao Peng +8
Many researchers have reached the conclusion that AI models should be trained to be aware of the possibility of variation and disagreement in human judgments, and evaluated as per…
Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora
Robert Litschko, Verena Blaschke, Diana Burkhardt +2
Dialects exhibit a substantial degree of variation due to the lack of a standard orthography. At the same time, the ability of Large Language Models (LLMs) to process dialects rema…
Do LLMs Give Psychometrically Plausible Responses in Educational Assessments?
Andreas Säuberli, Diego Frassinelli, Barbara Plank
Knowing how test takers answer items in educational assessments is essential for test development, to evaluate item quality, and to improve test validity. However, this process usu…
Mind the Uncertainty in Human Disagreement: Evaluating Discrepancies between Model Predictions and Human Responses in VQA
Jian Lan, Diego Frassinelli, Barbara Plank
Large vision-language models frequently struggle to accurately predict responses provided by multiple human annotators, particularly when those responses exhibit human uncertainty.…
To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity
Anastasiia Sedova, Robert Litschko, Diego Frassinelli +2
One of the major aspects contributing to the striking performance of large language models (LLMs) is the vast amount of factual knowledge accumulated during pre-training. Yet, many…