Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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The Divergent Remote Association Test (DRAT) is the first creativity test that significantly predicts LLMs' scientific ideation ability, unlike prior tests such as DAT or RAT.
CLIN uses perplexity, topic diversity, lexical clustering, and unigram count to evaluate originality, fluency, and elaboration in short Persian literary text, matching or exceeding zero-shot LLM judges at much lower cost.
citing papers explorer
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Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations
Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
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Assessing the Creativity of Large Language Models: Testing, Limits, and New Frontiers
The Divergent Remote Association Test (DRAT) is the first creativity test that significantly predicts LLMs' scientific ideation ability, unlike prior tests such as DAT or RAT.
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CLIN: an Objective Framework for Evaluating Creativity in Short Persian Literary Text
CLIN uses perplexity, topic diversity, lexical clustering, and unigram count to evaluate originality, fluency, and elaboration in short Persian literary text, matching or exceeding zero-shot LLM judges at much lower cost.
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