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cs.CL2026
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages
Nick McKenna, Xinnuo Xu, Jack Williams +3
A key consideration when training an LLM is whether the target language is more or less resourced, for example English compared to Welsh, or Python compared to Excel. Typical train…
cs.CL2025
Improving In-Context Learning with Reasoning Distillation
Nafis Sadeq, Xin Xu, Zhouhang Xie +4
Language models rely on semantic priors to perform in-context learning, which leads to poor performance on tasks involving inductive reasoning. Instruction-tuning methods based on…
cs.CL2024
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation
Shuting Wang, Xin Yu, Mang Wang +3
Retrieval-augmented generation (RAG) effectively addresses issues of static knowledge and hallucination in large language models. Existing studies mostly focus on question scenario…