The paper presents LMIQ, a pipeline for transforming psychological-interview transcripts into numerical variables. An LLM receives a transcript, adopts the interviewee's perspective, and answers questions with values from 1 to 5. The resulting 135 responses feed a Random Forest that predicts two self-report scores: PHQ-8 for depressive symptoms and PCL-C for post-traumatic stress symptoms. The main experiment uses the 275 E-DAIC interviews and its predefined splits. Features comprise 70 mental-health questions, 25 Big Five personality questions, 15 therapeutic questions, and 25 questions called direct. The non-direct items were initially generated with GPT-4 and manually refined; the paper provides no clinical or psychometric validation of these new questionnaires.
With GPT-3.5 Turbo Instruct, LMIQ obtains test MSEs of 20.42 for PHQ and 192.93 for PTSD, compared with 24.16 and 195.55 when the same model is asked to complete only the direct questionnaires and its answers are summed. Mixtral improves the main results to 18.05 and 163.75. These are regression errors on questionnaire scores, not measures of clinical diagnosis. The paper also compares direct GPT-3.5 prediction, Ada-002 embeddings, MentaLLaMA, TF-IDF, and the dataset mean. LMIQ-Mixtral is the strongest variant in the main table, but no repeated runs, confidence intervals, or statistical tests are reported to quantify the stability or significance of the differences.
The ablation qualifies the claim that pooling every domain is most effective. With GPT-3.5, the 135-feature set is not the best test configuration for either task: personality plus direct questions reaches 20.26 PHQ MSE and 166.62 PTSD MSE, versus 20.42 and 192.93 with all features. Therapeutic plus direct also improves PTSD to 168.62 and mental health plus direct to 177.15. Variants without direct questions are substantially worse. This supports the presence of predictive signal in simulated answers, but it also reveals heavy dependence on questions that closely proxy the target instruments and shows that adding every domain can be harmful, especially for PTSD.
The appendix audit finds an instrument-fidelity problem. The paper declares eight PHQ-8 questions plus seventeen PCL-C questions, yet the published PHQ list contains nine items because it adds “How likely are you to volunteer your time to help others?”, which is not part of the PHQ-8. The official PHQ-8 asks about frequency during the past two weeks and uses 0–3 scoring; LMIQ's uniform prompt requests 1–5 agreement and removes that time anchor. These features are therefore not faithful PHQ-8 completions but model-generated ordinal proxies. The official PCL-C does use 17 items rated 1–5, although the paper likewise does not precisely document preservation of its time and stressful-experience anchors. The paper itself acknowledges that impersonated responses are not validated against human answers.
The defensible contribution is an exploratory study showing that structured responses generated from an interview can be more predictive than several text representations on one benchmark split. It does not establish automatic diagnosis or clinical validity. Targets are self-report severity scores rather than clinician diagnoses; thresholds, sensitivity, specificity, calibration, subgroup analysis, uncertainty, and prospective evaluation are absent. No code, derived data, or executable configuration is released, and the exact Mixtral provider/version, snapshots, seeds, cost, failures, and retries are not documented. Sending sensitive transcripts to a commercial API is justified with a general statement about de-identification and compliance, without documenting consent for this processing, ethics review, retention, residency, a processing agreement, or re-identification testing. In its present form, LMIQ should be understood as experimental feature extraction for benchmark regression, not a system ready for clinical decisions.