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cs.CL2026
Measuring Intent Comprehension in LLMs
Nadav Kunievsky, James A. Evans
People judge interactions with large language models (LLMs) as successful when outputs match what they want, not what they type. Yet LLMs are trained to predict the next token sole…
cs.CL2026
Narrative Flattening: How Post-Training Compresses Thematic, Affective, and Stylistic Variation in LLM Fiction
Zehan Li, Yutong Zhu, Siyang Wu +2
Large language models produce fluent fiction, yet their creative output is widely seen as flat. We ask where this quality originates in the training and whether it affects differen…