Dep-LLM: Training-free depression diagnosis framework using structured multi-factor LLM reasoning
Dep-LLM is a training-free framework for automatic depression detection from clinical interviews that uses frozen foundation LLMs without fine-tuning. The system decomposes long clinical dialogues into five thematic factors via Chain-of-Thought analysis, applies token-level entropy-based confidence modulation, and integrates multi-factor signals for final diagnosis. Evaluated on DAIC-WOZ and E-DAIC datasets, it outperforms zero-shot baselines across 21 foundation LLMs and surpasses supervised domain-specific and commercial LLMs on multiple metrics.
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LLMs predict dementia and depression severity from clinical interview transcripts in zero-shot and feature-extraction settings
Researchers evaluate three open-weights LLMs (Mistral 3.1, DeepHermes, Qwen3) for predicting dementia and depression severity from speech transcripts of 154 German-speaking patients in standardized clinical interviews. The study introduces a new observer-based Global Depression Scale (GDS-D) and tests both zero-shot prediction and LLM-based feature extraction for Support Vector Regression. Zero-shot performs well for depression (MAE 0.60), while structured feature extraction reduces dementia assessment error by up to 35%; pause-enriched automatic transcripts match human transcription quality, suggesting viable fully-automated screening pipelines.
LLUMI: Fine-Tuning Open-Source LLMs for Mental Health Writing Assistance Using Reddit Community Feedback
LLUMI is a two-component system (a generation model and an improvement model) designed to provide mental health writing assistance using smaller open-source LLMs hosted in privacy-preserving, on-premise environments. The system leverages Reddit community endorsement signals (upvotes/downvotes) to construct preference pairs for SFT and DPO training, then further aligns outputs via human evaluation across readability, empathy, connection, actionability, and safety dimensions. Results show LLUMI achieves performance comparable to proprietary GPT-based models on linguistic and human evaluations, suggesting community-derived preference signals can substitute for expensive expert labeling in sensitive domains.
DEFINED: Data-efficient framework for fine-grained creativity assessment in debate using LLMs
DEFINED is a computational framework for automated creativity assessment in debate scenarios, operationalizing creativity through an eight-dimensional hierarchical metric system implemented via a pretrained autoregressive language model with a hierarchical scoring head. The system addresses data scarcity through constrained data augmentation and mixed-granularity training from limited expert-annotated data. It outperforms prompt-based LLM evaluators and existing debate scoring methods on authentic competition data. The work is relevant to AI evaluation methodology and the broader question of whether LLMs can reliably assess complex human cognitive outputs.
Fine-tuning LLMs to passively estimate depression severity from AI mental health conversations
Researchers fine-tune a Qwen3.5-27B model with a regression head to predict PHQ-9 depression severity scores directly from AI mental health app conversation transcripts, eliminating the need for explicit self-report completion. The training set of 6,283 users combines 3,111 ground-truth labels with pseudolabels generated by Claude Opus and iterative intermediate models. On a held-out test of 842 users, the best model achieves MAE=2.6, Pearson r=0.80, and AUC=0.91 at the clinical PHQ-9≥10 threshold, with AUC>0.87 across all severity thresholds. The work demonstrates a passive, continuous symptom-monitoring approach that could reduce response bias in mental health platforms.
LLM-Assisted Discovery of ADHD Signals in Turkish Teacher Narratives Beyond Rating Scales
This study analyzes de-identified Turkish teacher evaluation forms from clinical ADHD assessments, comparing predictive signals from structured rating scales (CTRS-R:S) and open-ended teacher narratives. The authors find that structured and narrative information encode complementary signals, with minimal overlap between cases missed by each modality. An LLM-assisted theme discovery pipeline reveals distinct attention, behavioral, and family-related patterns in narratives that structured scales miss, demonstrating NLP's potential to augment traditional ADHD screening.
Automated ICD Classification of Psychiatric Diagnoses Using NLP and LLMs
This study evaluates NLP and ML approaches for automating the mapping of free-text psychiatric descriptions to ICD diagnostic codes, using a dataset of 145,513 Spanish clinical records. Methods range from classical BoW/TF-IDF representations to transformer-based embeddings including e5_large, BioLORD, and Llama-3-8B. Fine-tuned e5_large achieved the best performance with a micro-F1 of 0.866, outperforming classical methods by capturing semantic nuance and medical terminology. The work highlights challenges of long-tail label distributions and ambiguity specific to psychiatric clinical language.
LLMSurgeon: Post-Hoc Auditing of LLM Pretraining Data Mixtures
LLMSurgeon formalizes Data Mixture Surgery (DMS), a framework for estimating the domain-level distribution of an LLM's pretraining corpus using only generated text from the target model. The method casts DMS as an inverse problem under the label-shift assumption, using a calibrated soft confusion matrix to correct domain confusion and recover the latent mixture prior. The authors also introduce LLMScan, a verifiable evaluation suite built from open-source LLMs with known pretraining mixtures, on which LLMSurgeon demonstrates high-fidelity recovery of domain compositions without access to training data.
Text Analytics Evaluation Framework: Benchmarking LLMs on Social Media NLP Tasks
Researchers introduce a 470-question evaluation framework to assess LLM performance on aggregated social media text, applied to Twitter datasets across sentiment analysis, hate speech detection, and emotion recognition. Results show performance degrades substantially as input scale exceeds 500 instances, particularly for open-weights models on numerical tasks. Multi-label and target-dependent scenarios also show notable performance drops, and task complexity progressively erodes accuracy from basic semantic identification to comparison and counting operations. The findings point to architectural bottlenecks in current LLMs for rigorous quantitative analysis over large text collections.