This preprint, listed on an author's page as accepted and forthcoming at LREC 2026, raises a useful concern: evaluating personality induction one trait at a time can conceal failure on the complete Big Five vector. It trains models on human essays labeled with the 32 binary OCEAN combinations and compares SFT, DPO, and ORPO using IPIP-NEO. Its tables report very low exact match, at most 3 of 32 profiles. However, the implementation does not support the paper's two strongest explanations: it neither demonstrates that fine-tuning stabilizes responses under prompt rephrasing nor that essays lack sufficient personality cues.
The described corpus has 2,467 essays, 1.9 million words, and about 770 words per text. The repository releases 2,066 unique moderated essays split 1,652/207/207 for train/validation/test. SFT receives the OCEAN vector and learns to generate the corresponding human essay; DPO/ORPO contrast it with three essays whose complete vector differs. Another condition appends synthetic responses to about half of IPIP-NEO. Reported models are Gemma-2-2B, Llama-3.2-3B, Gemma-7B, Llama-3.1-8B, and GPT-3.5-turbo-0125, plus unidentified “uncensored” counterparts.
The central problem affects RQ1. The code does not calculate variation for the same profile across S1, S2, and S3. It calculates the standard deviation of scores across 32 different target profiles inside each file. This dispersion measures sensitivity to the requested profile, not to rephrasing. A collapsed model that always answers identically would obtain zero deviation, the apparent optimum. The reported 15%-33% reduction can therefore mean weaker personality differentiation rather than stronger stability; the released statistic does not measure the rephrasing-robustness claim.
RQ2 also contains target leakage. After generating an essay, every questionnaire item again says “Answer the question as if you are” followed by the full target OCEAN vector. The essay is added as context, but the explicit label remains. Exact match mixes compliance with that instruction, essay effects, and self-report formatting; it does not infer a vector from an unguided essay. A 1/32 reference is reasonable for the artificially balanced 32 vectors, but each configuration has one pass. For 3/32, a binomial reference with p=1/32 gives P(X≥3)≈0.077 and a Wilson 95% interval of about 3.24%-24.22%, overlapping 0.55%-15.74% for 1/32. Moreover, 3/32 is the maximum across 36 configurations. There are no repeats, seeds, intervals, tests, multiplicity correction, or power analysis.
The safety conclusion uses only counts from zero to three between censored and “uncensored” models, without exact identities, snapshots, architectural matching, or an equivalence test. “No significant gains” does not establish no effect or remove alignment as a confounder. The two qualitative cases are not causal explanations either; the first quoted essay appears verbatim in the SFT training split.
Data and ethics claims conflict with the artifact. Moving from 2,467 to 2,066 leaves 401 records, not “approximately 300,” without a manifest. The script filters hate, harassment, self-harm, sexual, and violence categories; it does not detect PII although the paper attributes filtering to personal data. Essays contain names, locations, relationships, and health narratives; a conservative check finds 11 with “@,” nine mentioning a therapist, and at least 63 with selected health terms. No consent, lawful-basis, or re-identification audit supports the no-PII and GDPR claims. The paper assigns Apache-2.0 to the dataset without upstream evidence in the repository and promises MIT while shipping no LICENSE.
The questionnaire condition adds noise: it adjusts raw responses before reverse scoring. Of 10,330 randomized synthetic trait blocks, 210 fall in the wrong class under the study's threshold; 78 negative blocks land exactly at 3.0 and are classified positive. SFT train is also imbalanced, with 24-131 examples per vector.
The repository makes preprocessing, SFT/DPO/ORPO for four Hugging Face families, local inference, and processed data inspectable; all Python files compile syntactically. Missing components include questions.json, original/moderated CSVs, uncensored models, the GPT-3.5 pipeline, questionnaire DPO/ORPO data, checkpoints, raw responses and results, logs, seeds, tests, CI, lockfile, and container. The main text specifies DPO/ORPO rank 8, dropout 0.1, cosine scheduling, and 10% warmup; code and appendix specify rank 16, dropout 0.05, linear scheduling, and 500 steps. The faithful conclusion is limited: the work descriptively finds few complete vectors and correctly emphasizes joint exact match, but its stability metric is misaligned, evaluation leaks the target, and the artifact cannot reproduce the numbers. It does not establish latent personality, causal insufficiency of essays, safety equivalence, or legal and ethical compliance.