Creative Compression in LLM-Based Dramatic Character Interpretation

Large language models can generate multiple interpretations of dramatic characters, but it is unclear whether these interpretations preserve differences across characters. We compare a non-interpretive control with three interpretation conditions across characters from 40 public-domain English plays and two commercial LLMs. Explicit diversity prompting did not consistently increase diversity: it did not maximize within-response lexical spread, produced the lowest held-out lexical identifiability estimates, and showed the largest character n-gram convergence estimates. Yet word-level representations showed no general convergence, and grounding did not reliably improve held-out identifiability. These results show that diversity and character convergence depend on how textual difference is represented and measured.