# Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice

arXiv: https://arxiv.org/abs/2501.00982 · HTML: https://mlsanity.com/paper/2501.00982/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 74 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Long-context LLMs Struggle with Long In-context Learning](https://mlsanity.com/paper/2404.02060/) (2024)
- [Estimating the intrinsic dimension of datasets by a minimal neighborhood information](https://mlsanity.com/paper/1803.06992/) (2018)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [LLM Questionnaire Completion for Automatic Psychiatric Assessment](https://mlsanity.com/paper/2406.06636/) (2024)
- [Adapting Deep Learning Methods for Mental Health Prediction on Social Media](https://mlsanity.com/paper/2003.07634/) (2020)
- [Selecting which Dense Retriever to use for Zero-Shot Search](https://mlsanity.com/paper/2309.09403/) (2023)
- [Semantic Similarity Models for Depression Severity Estimation](https://mlsanity.com/paper/2211.07624/) (2022)
- [Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks](https://mlsanity.com/paper/2306.16891/) (2023)
- [Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification](https://mlsanity.com/paper/2405.15132/) (2024)
- [Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression](https://mlsanity.com/paper/2204.13569/) (2022)

## Built on by (most cited first, 11 of 11)

- [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)
- [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026)
- [Mental-R1: Aligning LLM Reasoning for Mental Health Assessment](https://mlsanity.com/paper/2606.13176/) (2026)
- [Learning Evidence of Depression Symptoms via Prompt Induction](https://mlsanity.com/paper/2604.24376/) (2026)
- [Navigating through the hidden embedding space: steering LLMs to improve mental health assessment](https://mlsanity.com/paper/2510.16373/) (2025)
- [Psychological Concept Neurons: Can Neural Control Bias Probing and Shift Generation in LLMs?](https://mlsanity.com/paper/2604.11802/) (2026)
- [Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support](https://mlsanity.com/paper/2606.30887/) (2026)
- [WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection](https://mlsanity.com/paper/2607.04350/) (2026)
- [From token probabilities to calibrated confidence: An empirical study of mathematical question answering](https://mlsanity.com/paper/2608.07827/) (2026)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)
- [PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media](https://mlsanity.com/paper/2610.15044/) (2026)

## Abstract (arXiv)

In psychological practices, standardized questionnaires serve as essential tools for assessing mental health through structured, clinically-validated questions (i.e., items). While social media platforms offer rich data for mental health screening, computational approaches often bypass these established clinical assessment tools in favor of black-box classification. We propose a novel questionnaire-guided screening framework that bridges psychological practice and computational methods through adaptive Retrieval-Augmented Generation (textitaRAG). Our approach links unstructured social media content and standardized clinical assessments by retrieving relevant posts for each questionnaire item and using Large Language Models (LLMs) to complete validated psychological instruments. Our findings demonstrate two key advantages of questionnaire-guided screening: First, when completing the Beck Depression Inventory-II (BDI-II), our approach matches or outperforms state-of-the-art performance on Reddit-based benchmarks without requiring training data. Second, we show that guiding LLMs through standardized questionnaires can yield superior results compared to directly prompting them for depression screening, while also providing a more interpretable assessment by linking model outputs to clinically validated diagnostic criteria. Additionally, we show, as a proof-of-concept, how our questionnaire-based methodology can be extended to other mental conditions' screening, highlighting the promising role of LLMs as psychological assessors.