PersLitEval benchmark shows LLMs perform better on conceptual Persian literature tasks than spelling or word formation, with explained few-shot prompting yielding the strongest results across six models.
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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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PersLitEval: Fine-grained Benchmark and Evaluation of LLMs on Persian Literature Questions
PersLitEval benchmark shows LLMs perform better on conceptual Persian literature tasks than spelling or word formation, with explained few-shot prompting yielding the strongest results across six models.
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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.