A Constrained Text Revision Agent via Iterative Planning and Searching

Existing text revision systems are capable of generating fluent and coherent text, but struggle with constrained text revision (CTR), which requires adherence to specific constraints. Furthermore, adapting these systems to diverse constraints is challenging. To bridge this gap, we introduce CRAFT, a Constrained Revision Agent For Text, focusing on CTR. CRAFT utilizes a planner, a reviser (i.e., a large language model), and adaptable tools to generate revisions tailored to different scenarios. Specifically, we propose an iterative self-training alignment method to construct the planner, which generates tool usage and text revision plans. Furthermore, we propose Tool-Guided Monte Carlo Tree Search (TG-MCTS), a novel CTR algorithm that extends MCTS with toolguided expansion and evaluation, enabling the search for optimal revision strategies across various scenarios. To evaluate CRAFT, we introduce CORD (COnstrained Revision Dataset), a dataset with multi-level constrained instructions for paragraph-level revision. Experimental results show that CRAFT outperforms baselines in both constraint adherence and revision quality. Furthermore, CRAFT exhibits robust performance across diverse use cases, including plain text and LaTeX revision. 1 Love it or hate it, Jared Leto's interpretation of the Joker is an internet ...…