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In this paper, we propose a novel method, Chain-of-Thoughts AttributeManipulation (CoTAM), to guide few-shot learning by carefully crafted data fromLarge Language Models (LLMs). The main idea is to create data with changes onlyin the attribute targeted by the task. Inspired by facial attributemanipulation, our approach generates label-switched data by leveraging LLMs tomanipulate task-specific attributes and reconstruct new sentences in acontrolled manner. Instead of conventional latent representation controlling,we implement chain-of-thoughts decomposition and reconstruction to adapt theprocedure to LLMs. Extensive results on text classification and other tasksverify the advantage of CoTAM over other LLM-based text generation methods withthe same number of training examples. Analysis visualizes the attributemanipulation effectiveness of CoTAM and presents the potential of LLM-guidedlearning with even less supervision.

Generating Efficient Training Data via LLM-based Attribute Manipulation