# Evidence-Driven Marker Extraction for Social Media Suicide Risk Detection

arXiv: https://arxiv.org/abs/2502.18823 · HTML: https://mlsanity.com/paper/2502.18823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2309.14717/) (2023)
- [Transformer Language Models without Positional Encodings Still Learn Positional Information](https://mlsanity.com/paper/2203.16634/) (2022)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [An Empirical Study on Information Extraction using Large Language Models](https://mlsanity.com/paper/2305.14450/) (2023)
- [Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models](https://mlsanity.com/paper/2410.19732/) (2024)
- [Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback](https://mlsanity.com/paper/2501.01377/) (2025)
- [Multimodal Event Transformer for Image-guided Story Ending Generation](https://mlsanity.com/paper/2301.11357/) (2023)
- [Diffusion Model with Representation Alignment for Protein Inverse Folding](https://mlsanity.com/paper/2412.09380/) (2024)
- [Information Extraction from Unstructured data using Augmented-AI and Computer Vision](https://mlsanity.com/paper/2312.09880/) (2023)
- [Learning Representation for Multitask learning through Self Supervised Auxiliary learning](https://mlsanity.com/paper/2409.16651/) (2024)

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

- [Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media](https://mlsanity.com/paper/2510.08365/) (2025)

## Abstract (arXiv)

Early detection of suicide risk from social media text is crucial for timely intervention. While Large Language Models (LLMs) offer promising capabilities in this domain, challenges remain in terms of interpretability and computational efficiency. This paper introduces Evidence-Driven LLM (ED-LLM), a novel approach for clinical marker extraction and suicide risk classification. ED-LLM employs a multi-task learning framework, jointly training a Mistral-7B based model to identify clinical marker spans and classify suicide risk levels. This evidence-driven strategy enhances interpretability by explicitly highlighting textual evidence supporting risk assessments. Evaluated on the CLPsych datasets, ED-LLM demonstrates competitive performance in risk classification and superior capability in clinical marker span identification compared to baselines including fine-tuned LLMs, traditional machine learning, and prompt-based methods. The results highlight the effectiveness of multi-task learning for interpretable and efficient LLM-based suicide risk assessment, paving the way for clinically relevant applications.