10 papers
Beyond Input Understanding: Diagnosing Multilingual Mathematical Reasoning with Directed Acyclic Trace Graphs
Jiaqiao Zhang, Zhoujun Li, Raoyuan Zhao +5
Large reasoning models (LRMs) achieve strong mathematical reasoning performance in English, but remain much less reliable in many low- and medium-resource languages. This gap is of…
ReverseMath: Answer Inversion for Scalable and Verifiable Mathematical Problem Generation
Raoyuan Zhao, Yihong Liu, Yupei Du +2
Mathematical reasoning benchmarks are vital for evaluating large language models (LLMs), but many are static and repeatedly exposed through public evaluation and training pipelines…
Crosslingual On-Policy Self-Distillation for Multilingual Reasoning
Yihong Liu, Raoyuan Zhao, Michael A. Hedderich +1
Large language models (LLMs) have achieved remarkable progress in mathematical reasoning, but this ability is not equally accessible across languages. Especially low-resource langu…
Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors
Raoyuan Zhao, Yihong Liu, Lena Altinger +2
Large language models (LLMs) are increasingly deployed in multilingual, real-world applications with user inputs -- naturally introducing \emph{typographical errors} (typos). Yet m…
Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners
Yihong Liu, Raoyuan Zhao, Hinrich Schütze +1
Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit chain-of-thought (CoT) explanati…
Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering
Jian Lan, Zhicheng Liu, Udo Schlegel +5
Large vision-language models (VLMs) achieve strong performance in Visual Question Answering but still rely heavily on supervised fine-tuning (SFT) with massive labeled datasets, wh…