arXiv:2605.25955v1 Announce Type: new Abstract: Large language models (LLMs) face a dual challenge in creative capability evaluation: existing benchmarks (e.g., Story Cloze Test, HellaSwag) measure models' discriminative ability over narrative continuation using multiple-choice recognition paradigms, rather than directly measuring creative generation capability; rubric-based scoring and LLM-as-Judge methods rely on subjective dimension assessment or natural language model outputs, and cannot provide objective, automated scoring mechanisms. This paper proposes QUIET (Quality Understanding via I
The rapid advancement and widespread deployment of large language models are creating an urgent need for more robust and objective methods to evaluate their creative capabilities, moving beyond discriminative tasks.
Improving the evaluation of LLM creativity through objective benchmarks is crucial for guiding research, development, and deployment of advanced AI, directly impacting the capabilities and trust in autonomous systems.
The introduction of the QUIET benchmark aims to provide an automated, less subjective, and direct measure of creative generation, potentially standardizing how LLM creativity is assessed.
- · AI researchers
- · LLM developers
- · Generative AI applications
- · AI safety researchers
- · Subjective LLM evaluation methods
- · Benchmarks focused solely on discriminative tasks
More accurate and consistent measurement of LLM creative potential will accelerate improvements in generative AI.
Standardized objective metrics could drive competition among LLM providers to demonstrate superior creative output, fostering innovation.
The development of truly creative AI could revolutionize content creation, design, and problem-solving across numerous industries, eventually contributing to the capabilities of AI agents.
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