AGC-Bench introduces a multi-domain creativity benchmark for LLMs, recovers a general 'c' factor explaining 81.5% of variance, and finds humans still outperform top models on matched tasks.
arXiv preprint arXiv:2509.09702 , year =
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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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AGC-Bench: Measuring Artificial General Creativity
AGC-Bench introduces a multi-domain creativity benchmark for LLMs, recovers a general 'c' factor explaining 81.5% of variance, and finds humans still outperform top models on matched tasks.
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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.