Zero Shot Learning With Task Specific Prompts And Label Semantics

via “few-shot and zero-shot task adaptation via in-context learning”

text-generation model by undefined. 1,06,54,004 downloads.

Unique: DeepSeek-V3.2 was trained with explicit in-context learning objectives, using diverse task examples during training to improve few-shot adaptation. The sparse MoE architecture allows task-specific experts to activate based on example patterns, improving few-shot performance without explicit task-specific fine-tuning.

vs others: Achieves 5-10% higher few-shot accuracy than Llama-2-70B on SuperGLUE and XTREME benchmarks due to specialized in-context learning training, while maintaining lower inference cost due to sparse activation