10 papers
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…
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…
Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA
Renhao Pei, Siyao Peng, Verena Blaschke +2
Large Language Models (LLMs) are becoming a common way for humans to seek knowledge, yet their coverage and reliability vary widely. Especially for local language varieties, there…
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…
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…
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…