LLM translations introduce model-specific statistically significant emotional fingerprints that limit preservation of author voice, with post-editing providing partial alignment to human norms.
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DEFINED is a data-efficient AI framework that uses a hierarchical eight-dimensional metric and mixed-granularity training on a pre-trained autoregressive LM to score debate creativity accurately from limited expert annotations.
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Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing
LLM translations introduce model-specific statistically significant emotional fingerprints that limit preservation of author voice, with post-editing providing partial alignment to human norms.
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DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios
DEFINED is a data-efficient AI framework that uses a hierarchical eight-dimensional metric and mixed-granularity training on a pre-trained autoregressive LM to score debate creativity accurately from limited expert annotations.