NARRA-Gym is an executable benchmark that generates complete interactive narrative episodes from emotional seeds and logs full model trajectories to expose gaps in coherence, adaptation, and personalization that static story tests miss.
arXiv preprint arXiv:2410.04197 , year =
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Large language models exhibit distinct creative patterns in molecule generation, including higher constraint satisfaction when more constraints are added, and this is the first work to reframe molecule generation abilities as creativity.
LLM translations introduce model-specific statistically significant emotional fingerprints that limit preservation of author voice, with post-editing providing partial alignment to human norms.
Proof-of-concept shows fine-tuned small language models achieve adequate quality for real-time game content generation in a scoped RPG loop via retry-until-success and LLM-as-judge evaluation.
citing papers explorer
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NARRA-Gym for Evaluating Interactive Narrative Agents
NARRA-Gym is an executable benchmark that generates complete interactive narrative episodes from emotional seeds and logs full model trajectories to expose gaps in coherence, adaptation, and personalization that static story tests miss.
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How Creative Are Large Language Models in Generating Molecules?
Large language models exhibit distinct creative patterns in molecule generation, including higher constraint satisfaction when more constraints are added, and this is the first work to reframe molecule generation abilities as creativity.
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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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High-quality generation of dynamic game content via small language models: A proof of concept
Proof-of-concept shows fine-tuned small language models achieve adequate quality for real-time game content generation in a scoped RPG loop via retry-until-success and LLM-as-judge evaluation.