The paper studies instructed socially desirable responding in LLM personality questionnaires and proposes a graded forced-choice (GFC) mitigation. Starting from 100 public-domain IPIP Big Five markers, GPT-5 and Gemini 2.5 Pro each provide 30 desirability ratings per item; after two voting items are excluded, optimization selects 30 cross-domain pairs containing 60 unique items with closely matched desirability. Fifty correlated synthetic Big Five vectors are discretized into stanines and verbalized through explicit adjective profiles. Nine models answer under HONEST and FAKE-GOOD instructions in both a 60-item Likert format and a 30-pair GFC format. Every item or pair is a separate API call that repeats the entire profile, rather than a continuous questionnaire session or spontaneously observed personality.
The released data contain 54,000 complete Likert responses and 26,999 of 27,000 expected GFC responses. Across the 45 model-by-trait cells, every desirability-direction-corrected Likert effect is positive, ranging from d=0.628 to 1.695 with mean 1.116. GFC sharply reduces that tendency: effects range from -0.917 to 0.666, with signed mean -0.089 and mean absolute effect 0.298. Honest profile recovery against the authors' continuous generated z target is higher for Likert (model means 0.866-0.947) than GFC (0.420-0.695). The defensible contribution is evidence that desirability matching and comparative judgment attenuate the average shift toward looking good, while losing information and not making each trait invariant.
The audit finds important limitations hidden by the main aggregation. Figure 4 averages the five signed trait effects before applying absolute practical thresholds: for GPT-5, A=0.462 and C=0.360 cancel against N=-0.388 and O=-0.873, yielding a -0.058 mean labeled practically negligible despite large profile changes. GPT-5 mini reaches -0.917 on N and Gemini 2.5 Pro -0.755 on O. The IRT models pool all nine models and both conditions under shared item parameters without testing measurement invariance or differential item functioning. The Likert GRM also retains 245 parameters above the declared Rhat 1.01 threshold, with maximum 1.0348 and minimum bulk ESS 115.55, although the manuscript only says convergence was monitored and the pipeline proceeds to posterior means. Tests, correlations and plotted intervals then treat those theta means as error-free observations rather than propagating posterior uncertainty.
The sole allegedly missing Claude Sonnet GFC answer is actually present in the published raw CSV and ends in the valid value 3; a stale derivative turns it into NA, the retry was never retrieved and the analysis removes all five theta values for that persona-condition. The OSF release is broad -R/Stan code, data, provider states, fitted models, diagnostics and figures- and its revised figures hash-match arXiv v2, but the project is unregistered and mutable, has no node license and does not freeze R, package or CmdStan versions. OpenAI and Gemini were called through provider aliases and defaults without released proof of the immutable snapshots suggested by the paper's table. The study supports GFC as a promising mitigation in this explicit synthetic design; it does not establish general absence of bias, full profile preservation, stable personality, spontaneous SDR or cross-model, cross-cultural and deployment-level psychometric validity.