arXiv:2606.30556v1 Announce Type: new Abstract: Traditional automatic evaluation methods have been shown to be unsuitable for modern Chinese poetry because of the distinct nature of this literary genre. Human evaluation remains reliable, but is expensive and not applicable to large-scale data. In this paper, we propose Poller (Poetry LLM Evaluator), a novel method leveraging large language models (LLMs) to evaluate the poetry understanding task. Specifically, our method requires LLMs to play the role of a poem's author with detailed information, thereby emulating human evaluation and judgment
The rapid advancement and sophistication of large language models (LLMs) enable them to perform complex cognitive tasks, making this evaluation method feasible now.
This development offers a scalable and potentially more objective method for evaluating nuanced tasks like poetry understanding, overcoming the limitations of traditional and human evaluation.
The ability to leverage LLMs for evaluating complex, subjective tasks introduces a new paradigm for quality assessment in AI-generated content and human-computer interaction.
- · AI developers
- · Content creators
- · Academic researchers
- · Traditional evaluation firms
LLMs will be increasingly used for nuanced content evaluation in fields beyond poetry.
The development of more sophisticated LLM evaluation frameworks will accelerate, leading to better AI performance across creative domains.
This could democratize access to high-quality evaluation, fostering a new wave of creative expression and AI-assisted content generation.
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Read at arXiv cs.CL