# Learning Evidence of Depression Symptoms via Prompt Induction

arXiv: https://arxiv.org/abs/2604.24376 · HTML: https://mlsanity.com/paper/2604.24376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [Instruction Induction: From Few Examples to Natural Language Task Descriptions](https://mlsanity.com/paper/2205.10782/) (2022)
- [MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models](https://mlsanity.com/paper/2309.13567/) (2023)
- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (2022)
- [Depression Detection on Social Media with Large Language Models](https://mlsanity.com/paper/2403.10750/) (2024)
- [Prompting with Pseudo-Code Instructions](https://mlsanity.com/paper/2305.11790/) (2023)
- [Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice](https://mlsanity.com/paper/2501.00982/) (2025)
- [Explainable Depression Symptom Detection in Social Media](https://mlsanity.com/paper/2310.13664/) (2023)
- [Prompt-MII: Meta-Learning Instruction Induction for LLMs](https://mlsanity.com/paper/2510.16932/) (2025)
- [Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation](https://mlsanity.com/paper/2505.20014/) (2025)
- [ReDSM5: A Reddit Dataset for DSM-5 Depression Detection](https://mlsanity.com/paper/2508.03399/) (2025)
- [Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media](https://mlsanity.com/paper/2407.09019/) (2024)

## Built on by (most cited first, 1 of 1)

- [Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity](https://mlsanity.com/paper/2609.39049/) (2026)

## Abstract (arXiv)

Depression places substantial pressure on mental health services, and many people describe their experiences outside clinical settings in high-volume user-generated text (e.g., online forums and social media). Automatically identifying clinical symptom evidence in such text can therefore complement limited clinical capacity and scale to large populations. We address this need through sentence-level classification of 21 depression symptoms from the BDI-II questionnaire, using BDI-Sen, a dataset annotated for symptom relevance. This task is fine-grained and highly imbalanced, and we find that common LLM approaches (zero-shot, in-context learning, and fine-tuning) struggle to apply consistent relevance criteria for most symptoms. We propose Symptom Induction (SI), a novel approach which compresses labeled examples into short, interpretable guidelines that specify what counts as evidence for each symptom and uses these guidelines to condition classification. Across four LLM families and eight models, SI achieves the best overall weighted F1 on BDI-Sen, with especially large gains for infrequent symptoms. Cross-domain evaluation on an external dataset further shows that induced guidelines generalize across other diseases shared symptomatology (bipolar and eating disorders).