核心概念
Large language models can be distilled into small language models by incorporating self-evaluation capability and comprehensive thinking, improving performance in resource-constrained environments.
摘要
The content discusses the challenges of deploying large language models (LLMs) in resource-constrained environments due to their scale and computational demands. It introduces a methodology to distill self-evaluation capability and comprehensive thinking from LLMs into small language models (SLMs) to enhance their performance. The method involves generating diverse CoTs and self-evaluation outputs from LLMs, training SLMs with multi-task learning, and conducting experiments on three NLP benchmarks to validate the effectiveness of the approach.
Abstract:
LLMs have advanced in natural language processing.
Challenges in practical deployment due to scale and computational demands.
Proposed methodology for distilling self-evaluation capability and comprehensive thinking into SLMs.
Experiments show improved performance of distilled SLMs.
Introduction:
Increase in parameters of LLMs leading to successes in NLP.
Challenges in practical application, especially in resource-limited environments.
Various studies focus on compressing LLMs into SLMs using knowledge distillation techniques.
Introduction of chain-of-thought (CoT) distillation method.
Data Extraction:
"Large language models are few-shot learners."
"Self-consuming generative models go mad."
統計資料
"Large language models are few-shot learners."
"Self-consuming generative models go mad."
引述
"Large language models are few-shot learners."
"Self-consuming generative models go mad."