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CCSBench: Evaluating Compositional Controllability in LLMs for Scientific Document Summarization

16 October 2024
Yixi Ding
Jiaying Wu
Tongyao Zhu
Yanxia Qin
Qian Liu
Min-Yen Kan
    CoGe
ArXiv (abs)PDFHTML
Abstract

To broaden the dissemination of scientific knowledge to diverse audiences, scientific document summarization must simultaneously control multiple attributes such as length and empirical focus. However, existing research typically focuses on controlling single attributes, leaving the compositional control of multiple attributes underexplored. To address this gap, we introduce CCSBench, a benchmark for compositional controllable summarization in the scientific domain. Our benchmark enables fine-grained control over both explicit attributes (e.g., length), which are objective and straightforward, and implicit attributes (e.g., empirical focus), which are more subjective and conceptual. We conduct extensive experiments on GPT-4, LLaMA2, and other popular LLMs under various settings. Our findings reveal significant limitations in large language models' ability to balance trade-offs between control attributes, especially implicit ones that require deeper understanding and abstract reasoning.

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