7 papers · 1 filter
SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization
Usman Naseem, Robert Geislinger, Juan Ren +31
We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances. Each data instance is multi-labe…
Self-Calibrating Language Models via Test-Time Discriminative Distillation
Mohamed Rissal Hedna, Jan Strich, Martin Semmann +1
Large language models (LLMs) are systematically overconfident: they routinely express high certainty on questions they often answer incorrectly. Existing calibration methods either…
LEMUR: A Corpus for Robust Fine-Tuning of Multilingual Law Embedding Models for Retrieval
Narges Baba Ahmadi, Jan Strich, Martin Semmann +1
Large language models (LLMs) are increasingly used to access legal information. Yet, their deployment in multilingual legal settings is constrained by unreliable retrieval and the…
Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA
Klejda Alushi, Jan Strich, Chris Biemann +1
Conversational question answering increasingly relies on retrieval-augmented generation (RAG) to ground large language models (LLMs) in external knowledge. Yet, most existing studi…
POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization
Usman Naseem, Robert Geislinger, Juan Ren +40
Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We…
EncouRAGe: Evaluating RAG Local, Fast, and Reliable
Jan Strich, Adeline Scharfenberg, Chris Biemann +1
We introduce EncouRAGe, a comprehensive Python framework designed to streamline the development and evaluation of Retrieval-Augmented Generation (RAG) systems using Large Language…