LLM-Enhanced Modeling of Social Desirability-Aware Forced-Choice Personality Assessment

Personality assessment serves as a key building block in intelligent information systems that enable human-centered modeling. Unlike cognitive tests, personality assessments rely primarily on self-reports and are therefore susceptible to faking. Forced-choice (FC) formats partially mitigate this problem, yet socially desirable responding remains a systematic source of bias. Traditional approaches rely on expert-annotated social desirability (SD) ratings to construct FC item blocks and infer respondents’ personality traits from block-level rankings. This rating procedure is labor-intensive and coarse-grained. Furthermore, existing methods neglect the non-linear SD interactions between respondents and items, which act as structured adversarial noise that hinders the recovery of true latent traits. To address these challenges, we propose the Social Desirability-aware Forced-Choice Diagnosis (SDFCD) approach. Our approach adopts a knowledge-guided learning paradigm by leveraging large language models (LLMs) to distill fine-grained, continuous SD ratings, thereby replacing sparse expert ratings. We then introduce a decoupled neural interaction module that jointly represents latent personality traits and SD tendencies, enabling the modeling of respondent–item SD interactions. Experiments on real assessment data demonstrate that our method significantly outperforms baseline FC models in personality trait diagnostic performance and model interpretability. This study highlights the potential of LLMs for automated, fine-grained SD quantification and offers a scalable path toward more trustworthy personality assessment.