3 papers
cs.CL2026
Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?
Renhao Pei, Yihong Liu, Sampo Pyysalo +2
Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic resources through in-context le…
cs.CL2026
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…
cs.CL2025
Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu
Renhao Pei, Yihong Liu, Peiqin Lin +2
In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT, as it can readily take advantage of linguistic resources such as…