7 papers
Efficient Multilingual Reasoning Transfer via Progressive Code-Switching
Zhijun Wang, Junxiao Liu, Hao Zhou +3
Large reasoning models (LRMs) have achieved strong reasoning capabilities in English, yet their performance degrades significantly when required to reason in other languages. A nat…
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM Integration into Upcycled MoE
Hao Zhou, Tianhao Li, Zhijun Wang +6
Expanding Large Language Models~(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training~(CPT) and data-intensive alignment. While recent data-free…
Language as a Latent Variable for Reasoning Optimization
Linjuan Wu, Haoran Wei, Jialong Tang +4
As LLMs reduce English-centric bias, a surprising trend emerges: non-English responses sometimes outperform English on reasoning tasks. We hypothesize that language functions as a…
Judge Like Human Examiners: A Weighted Importance Multi-Point Evaluation Framework for Generative Tasks with Long-form Answers
Guoxin Yu, Chulun Zhou, Lemao Liu +7
Evaluating the quality of model responses remains challenging in generative tasks with long-form answers, as the expected answers usually contain multiple semantically distinct yet…
Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training
Linjuan Wu, Haoran Wei, Huan Lin +4
Large language models (LLMs) exhibit remarkable multilingual capabilities despite English-dominated pre-training, attributed to cross-lingual mechanisms during pre-training. Existi…
Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation
Suhang Wu, Jialong Tang, Baosong Yang +5
RALMs (Retrieval-Augmented Language Models) broaden their knowledge scope by incorporating external textual resources. However, the multilingual nature of global knowledge necessit…