3 citations · 4 across the 24 of their papers we have counts for
25 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…
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…
Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective
Beiduo Chen, Tiancheng Hu, Caiqi Zhang +3
Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation--which requires capturing probabilistic am…
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…