5 papers
Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection
Ahmad Dawar Hakimi, Lea Hirlimann, Isabelle Augenstein +1
Instruction-tuned LLMs can annotate thousands of instances at low cost. This raises two questions for active learning (AL): can LLM labels replace human labels within the AL loop,…
MemeScouts@LT-EDI 2026: Asking the Right Questions -- Prompted Weak Supervision for Meme Hate Speech Detection
Ivo Bueno, Lea Hirlimann, Enkelejda Kasneci
Detecting hate speech in memes is challenging due to their multimodal nature and subtle, culturally grounded cues such as sarcasm and context. While recent vision-language models (…
On Relation-Specific Neurons in Large Language Models
Yihong Liu, Runsheng Chen, Lea Hirlimann +6
In large language models (LLMs), certain \emph{neurons} can store distinct pieces of knowledge learned during pretraining. While factual knowledge typically appears as a combinatio…
SLAyiNG: A Diverse and Community-validated Dataset of Queer Slang
Leonor Veloso, Lea Hirlimann, Philipp Wicke +4
Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language. Because of this, NLP systems often process q…
Robustness Testing of Multi-Modal Models in Varied Home Environments for Assistive Robots
Lea Hirlimann, Shengqiang Zhang, Hinrich Schütze +1
The development of assistive robotic agents to support household tasks is advancing, yet the underlying models often operate in virtual settings that do not reflect real-world comp…