ConCISE: The New Reference-Free Metric for Taming LLM Verbosity and Boosting Efficiency
By Seyed Mohssen Ghafari, Ronny Kol, Juan C. Quiroz, Nella Luan, Monika Patial, Chanaka Rupasinghe, Herman Wandabwa, Luiz Pizzato
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CL updates on arXiv.org.
Summary
Large language models (LLMs) frequently produce verbose and redundant answers, diminishing user satisfaction and increasing operational costs due to token-based pricing. To address this, researchers introduce ConCISE, a novel reference-free metric designed to evaluate the conciseness of LLM-generated responses without requiring gold standard human annotations. ConCISE quantifies non-essential content by averaging three distinct compression calculations: a compression ratio between the original response and an LLM-generated abstractive summary, another between the original response and an LLM-generated extractive summary, and a word-removal compression score where an LLM prunes non-essential words while preserving meaning. Experimental results confirm ConCISE's ability to accurately identify redundancy, providing an automated and practical tool for assessing response brevity in conversational AI systems.
Why It Matters
This development is crucial for professionals in the AI industry for several compelling reasons, signifying a notable stride in LLM operationalization and user experience. Firstly, the direct link between verbosity and increased token costs means that an effective, automated conciseness metric like ConCISE can lead to significant cost savings for businesses deploying proprietary LLMs, making AI applications more economically viable at scale. Secondly, conciseness is a cornerstone of user satisfaction; verbose outputs can frustrate users and erode trust, particularly in critical applications like customer service or information retrieval. ConCISE provides a quantifiable way to ensure LLMs deliver clear, efficient communication, directly impacting the quality of human-AI interaction.
More broadly, the "reference-free" nature of ConCISE is a game-changer for LLM development and evaluation. Traditional evaluation often relies on costly, time-consuming human annotations or large, pre-existing datasets (gold standards) which are often scarce, especially in niche domains. By eliminating this dependency, ConCISE democratizes LLM evaluation, allowing developers to rapidly iterate, fine-tune, and benchmark models for conciseness without significant overhead. This accelerates the development cycle, enabling quicker deployment of more efficient and user-friendly LLMs. Furthermore, it pushes the industry's focus beyond mere factual accuracy to encompass critical aspects of output utility and efficiency, offering a holistic perspective on LLM quality. As AI systems become more pervasive, metrics that optimize for practical utility and resource efficiency, like ConCISE, are indispensable for driving sustainable and impactful AI innovation.