Grimoire is All You Need for Enhancing Large Language Models
This addresses the challenge of enhancing ICL for weaker language models, which is incremental as it builds on existing ICL techniques.
The paper tackles the problem of inconsistent in-context learning (ICL) performance in weak language models by proposing SLEICL, a method where strong models learn from examples and transfer skills to weak models, resulting in weak models consistently improving over their zero-shot/few-shot capabilities and sometimes surpassing GPT4-1106-preview (zero-shot).
In-context Learning (ICL) is one of the key methods for enhancing the performance of large language models on specific tasks by providing a set of few-shot examples. However, the ICL capability of different types of models shows significant variation due to factors such as model architecture, volume of learning data, and the size of parameters. Generally, the larger the model's parameter size and the more extensive the learning data, the stronger its ICL capability. In this paper, we propose a method SLEICL that involves learning from examples using strong language models and then summarizing and transferring these learned skills to weak language models for inference and application. This ensures the stability and effectiveness of ICL. Compared to directly enabling weak language models to learn from prompt examples, SLEICL reduces the difficulty of ICL for these models. Our experiments, conducted on up to eight datasets with five language models, demonstrate that weak language models achieve consistent improvement over their own zero-shot or few-shot capabilities using the SLEICL method. Some weak language models even surpass the performance of GPT4-1106-preview (zero-shot) with the aid of SLEICL.