arxiv preprint - Tree Prompting: Efficient Task Adaptation without Fine-Tuning

In this episode, we discuss Tree Prompting: Efficient Task Adaptation without Fine-Tuning by John X. Morris, Chandan Singh, Alexander M. Rush, Jianfeng Gao, Yuntian Deng. Tree Prompting is a novel method for interacting with smaller language models (LMs) that creates a decision tree of prompts to guide the model's responses. This technique significantly enhances accuracy on tasks compared to traditional prompting methods and rivals the performance of gradient-based fine-tuning. Additionally, some versions of Tree Prompting provide insights into the LM's decision-making process.