# Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning

arXiv: https://arxiv.org/abs/2606.10796 · HTML: https://mlsanity.com/paper/2606.10796/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 34 of its 63 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 30 of 34)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://mlsanity.com/paper/2402.10373/) (2024)
- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models](https://mlsanity.com/paper/2309.13567/) (2023)
- [Topic Modeling Based Multi-modal Depression Detection](https://mlsanity.com/paper/1803.10384/) (2018)
- [Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity](https://mlsanity.com/paper/2506.00245/) (2025)
- [PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation](https://mlsanity.com/paper/2407.05721/) (2024)
- [Depression Detection on Social Media with Large Language Models](https://mlsanity.com/paper/2403.10750/) (2024)
- [DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection](https://mlsanity.com/paper/2409.15936/) (2024)
- [MAGI: Multi-Agent Guided Interview for Psychiatric Assessment](https://mlsanity.com/paper/2504.18260/) (2025)
- [Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs](https://mlsanity.com/paper/2509.13813/) (2025)
- [HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection](https://mlsanity.com/paper/2408.03648/) (2024)
- [Uncertainty Estimation of Large Language Models in Medical Question Answering](https://mlsanity.com/paper/2407.08662/) (2024)
- [Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models](https://mlsanity.com/paper/2502.05879/) (2025)
- [Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy](https://mlsanity.com/paper/2503.00269/) (2025)
- [SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information](https://mlsanity.com/paper/2502.10950/) (2025)
- [Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents](https://mlsanity.com/paper/2504.02800/) (2025)
- [AgentMental: An Interactive Multi-Agent Framework for Explainable and Adaptive Mental Health Assessment](https://mlsanity.com/paper/2508.11567/) (2025)
- [WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis](https://mlsanity.com/paper/2502.20689/) (2025)
- [Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.01315/) (2025)
- [Large Language Models for Depression Recognition in Spoken Language Integrating Psychological Knowledge](https://mlsanity.com/paper/2505.22863/) (2025)

## Built on by (most cited first, 2 of 2)

- [HiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical Interviews](https://mlsanity.com/paper/2608.21868/) (2026)
- [A Deterministic Evidence Layer for Vision-Language Autism Screening from Naturalistic Home Video](https://mlsanity.com/paper/2610.09217/) (2026)

## Abstract (arXiv)

Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two critical obstacles: 1) difficulty in modeling complex but sparsely distributed depression clues within lengthy, multi-topic clinical interviews, leading to superficial and unreliable reasoning; 2) scarcity of labeled data due to clinical privacy, together with high cost of training and fine-tuning, limiting the deployment of supervised ADD systems. To jointly address these challenges, we propose Dep-LLM, a training-free framework that mirrors the step-by-step reasoning of clinical psychiatrists and operates entirely on frozen off-the-shelf foundation LLMs. Dep-LLM comprises three stages. First, a Chain-of-Thought (CoT) Depression Multi-factor Analysis module structurally decomposes the long dialogue into five clinically aligned themes and produces evidence-grounded rationales, effectively handling long-context dependencies. Second, we introduce Confidence Analysis and Modulation module that quantifies the epistemic reliability from token-level entropy of each rationale and applies an intra-label and inter-theme modulation that amplifies trustworthy signals while suppressing uncertain ones without extra training. Third, a Collaborative Multi-factor Prediction module dynamically integrates multi-factor signals weighted by confidence into the final diagnosis. Extensive experiments on the DAIC-WOZ and E-DAIC datasets demonstrate the effectiveness and generalizability of Dep-LLM: it surpasses zero-shot baseline on nearly all 21 foundation LLMs across 9 metrics such as accuracy, macro F1 and weighted-average F1, and further outperforms state-of-the-art supervised domain-specific LLMs as well as the latest closed-source commercial LLMs, while requiring no extra training.