1 citations · 6 across the 30 of their papers we have counts for
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EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI
Longfei Zuo, Barbara Plank, Siyao Peng
High-quality datasets are critical for training and evaluating reliable NLP models. In tasks like natural language inference (NLI), human label variation (HLV) arises when multiple…
BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)
Tomas Ruiz, Siyao Peng, Barbara Plank +1
Test-time scaling is a family of techniques to improve LLM outputs at inference time by performing extra computation. To the best of our knowledge, test-time scaling has been limit…
Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations
Pingjun Hong, Beiduo Chen, Siyao Peng +3
Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches analyze the underlying reasoning…
Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects
Verena Blaschke, Miriam Winkler, Barbara Plank
Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. However, dialects are primarily spoken, and non-standard…
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…
Evaluating Large Language Models for Cross-Lingual Retrieval
Longfei Zuo, Pingjun Hong, Oliver Kraus +2
Multi-stage information retrieval (IR) has become a widely-adopted paradigm in search. While Large Language Models (LLMs) have been extensively evaluated as second-stage reranking…
Reason to Rote: Rethinking Memorization in Reasoning
Yupei Du, Philipp Mondorf, Silvia Casola +3
Large language models readily memorize arbitrary training instances, such as label noise, yet they perform strikingly well on reasoning tasks. In this work, we investigate how lang…
A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation
Verena Blaschke, Miriam Winkler, Constantin Förster +2
Although Germany has a diverse landscape of dialects, they are underrepresented in current automatic speech recognition (ASR) research. To enable studies of how robust models are t…
Threading the Needle: Reweaving Chain-of-Thought Reasoning to Explain Human Label Variation
Beiduo Chen, Yang Janet Liu, Anna Korhonen +1
The recent rise of reasoning-tuned Large Language Models (LLMs)--which generate chains of thought (CoTs) before giving the final answer--has attracted significant attention and off…
LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference
Pingjun Hong, Beiduo Chen, Siyao Peng +2
There is increasing evidence of Human Label Variation (HLV) in Natural Language Inference (NLI), where annotators assign different labels to the same premise-hypothesis pair. Howev…
What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse
Shijia Zhou, Siyao Peng, Simon M. Luebke +4
Media framing refers to the emphasis on specific aspects of perceived reality to shape how an issue is defined and understood. Its primary purpose is to shape public perceptions of…
Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent Detection
Verena Blaschke, Felicia Körner, Barbara Plank
Slot and intent detection (SID) is a classic natural language understanding task. Despite this, research has only more recently begun focusing on SID for dialectal and colloquial v…
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study
Xaver Maria Krückl, Verena Blaschke, Barbara Plank
Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-r…
Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages
Robert Litschko, Oliver Kraus, Verena Blaschke +1
A large amount of local and culture-specific knowledge (e.g., people, traditions, food) can only be found in documents written in dialects. While there has been extensive research…