Writing Like the Best: Exemplar-Based Expository Text Generation

• URL: http://arxiv.org/abs/2505.18859v1
• Date: Sat, 24 May 2025 20:40:39 GMT
• Title: Writing Like the Best: Exemplar-Based Expository Text Generation
• Authors: Yuxiang Liu, Kevin Chen-Chuan Chang,
• Abstract summary: We introduce the Exemplar-Based Expository Text Generation task, aiming to generate an expository text on a new topic using an exemplar on a similar topic.<n>Current methods fall short due to their reliance on extensive exemplar data, difficulty in adapting topic-specific content, and issues with long-text coherence.<n>We propose the concept of Adaptive Imitation and present a novel Recurrent Plan-then-Adapt framework.
• Score: 23.631195575124924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the Exemplar-Based Expository Text Generation task, aiming to generate an expository text on a new topic using an exemplar on a similar topic. Current methods fall short due to their reliance on extensive exemplar data, difficulty in adapting topic-specific content, and issues with long-text coherence. To address these challenges, we propose the concept of Adaptive Imitation and present a novel Recurrent Plan-then-Adapt (RePA) framework. RePA leverages large language models (LLMs) for effective adaptive imitation through a fine-grained plan-then-adapt process. RePA also enables recurrent segment-by-segment imitation, supported by two memory structures that enhance input clarity and output coherence. We also develop task-specific evaluation metrics--imitativeness, adaptiveness, and adaptive-imitativeness--using LLMs as evaluators. Experimental results across our collected three diverse datasets demonstrate that RePA surpasses existing baselines in producing factual, consistent, and relevant texts for this task.

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