One Arrow, Many Targets: Probing LLMs for Multi-Attribute Controllable Text Summarization
The paper "**One Arrow, Many Targets**" introduces a groundbreaking approach to *controllable text summarization* using Large Language Models. The research explores how a single prompt can simultaneously control multiple attributes of generated summaries, such as **length**, **style**, **focus**, and **tone**. The authors develop a novel *multi-attribute control framework* that enables precise manipulation of summary characteristics without requiring model fine-tuning or additional training. Through extensive experiments across various domains and document types, they demonstrate that LLMs can effectively generate summaries with specified combinations of attributes while maintaining content accuracy and coherence. The study reveals important insights about **prompt engineering strategies** and the inherent capabilities of LLMs in handling complex, multi-dimensional summarization tasks. This work significantly advances the field of controllable text generation, offering practical applications in personalized content creation and automated documentation systems.
