Controllable without Stereotyping: Cross-Context Stability, Utility Preservation, and Uncertainty in Big-Five-Conditioned LLM Dialogue
DOI:
https://doi.org/10.61424/gwmd8w43Keywords:
Big Five personality, large language models, persona control, response selection, calibration, selective abstention, personality caricatureAbstract
Personality-controllable dialogue systems must express intended traits without losing contextual fidelity or collapsing into formulaic cues. We evaluated 100,000 BIG5-CHAT responses representing 10,000 scenarios crossed with five Big Five dimensions and high and low levels. After listwise removal of 64 incomplete scenarios, 9,936 scenarios remained. Scenario-grouped training, validation, and test partitions contained 6,955, 1,490, and 1,491 scenarios. Three locally fitted response-selection strategies were compared: a fixed cue-based persona-prompt proxy, a word-TF-IDF weighted-nearest-neighbor persona retriever, and a character-ngram logistic lightweight adapter. Evaluation combined response-level discrimination, paired high-versus-low ordering, calibration, relation-wise stability, selective abstention, content-fidelity proxies, cue and name masking, length controls, and cross-trait entanglement. On the held-out test set, paired accuracy was 0.7797, 0.9896, and 0.9935 for prompt, retrieval, and adapter, respectively; corresponding AUROC values were 0.7993, 0.9927, and 0.9958. The adapter achieved the lowest Brier score (0.0234), while retrieval had the lowest expected calibration error (0.0066). Worst-relation paired accuracy remained 0.7602, 0.9824, and 0.9889. At 90% coverage, selection risk fell from 0.1892 for the prompt proxy to 0.0003 for retrieval and 0.0000 for the adapter. However, length alone ordered 67.3% of pairs, and cross-trait entanglement was higher for retrieval (0.2712) and adaptation (0.2897) than for prompting (0.0933). The findings demonstrate strong, stable corpus-level persona discrimination but also expose a control-entanglement trade-off; they do not establish demographic stereotype safety or generative-model confidence.
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