Large Language Models (LLMs) have been observed to perform well on a wide range of downstream tasks when fine-tuned on domain-specific data. However, such data may not be readily available in many applications, motivating zero-shot or few-shot approaches using domain-adjacent models. While several fine-tuned models for various tasks are available, finding an appropriate domain-adjacent model for a given task is often not straight forward. In this paper, we study DAFT-E, a framework that utilizes an Ensemble of Domain-Adjacent Fine-Tuned Foundation Models for few-shot problems. We show that for zero-shot problems, this ensembling method provides an accuracy performance close to that of the single best model. With few-shot problems, this performance improves further, at which point DEFT-E can outperform any single domain-adjacent model while requiring much less data for domain-specific fine-tuning.

利用领域相邻模型进行零样本或少样本学习的框架DAFT-E在零样本问题上表现接近单一最佳模型的准确性，在少样本问题上性能进一步提升，能够胜过任何单一领域相邻模型，同时需要更少的领域特定数据进行微调。

关于采用领域相邻的精调模型集合在少样本问题中的效果的实用性