Improving LLM Prompting with Ensemble of Instructions: A Case Study on Sentiment Analysis

In the era of large language models, exploring the capability of a large language model is a trending research direction where one is prompt engineering aiming at drafting the best instructions to ask a large language model. We assume that a selected large language model has a certain knowledge and capability of a given task, and the way to ask is strongly related to the task’s performance. This is certainly useful when we don’t have sufficient resource for finetuning or aligning the large language model. Following that direction, we present our approach towards improving the effectiveness of prompting a large language model by finding an optimal ensemble of instructions by using the large language model’s self-generated instructions and labeled data. A case study on sentiment analysis is carried out in a preliminary experiment. The positive results of our case study shows that the approach is promising.