New DiSP framework speeds up in-context learning for LLMs

Researchers have developed a new framework called DiSP to improve the efficiency of in-context learning (ICL) in large language models. DiSP addresses the challenge of selecting optimal demonstrations for prompts, which is computationally expensive. The framework stratifies queries by difficulty, uses random trials to estimate success rates, and trains a lightweight router to predict query difficulty. This approach allows for faster, more accurate demonstration selection compared to existing methods, achieving significant speedups and accuracy improvements on classification tasks with models like Llama 3 and Qwen 2.5. AI

影响 Improves efficiency of in-context learning, potentially reducing computational costs for LLM applications.

排序理由 The cluster contains an arXiv paper detailing a new framework for improving LLM in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

• Llama 3
• Qwen 2.5

• 论文
• 模型发布

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