arXiv Machine Learning

Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity

arXiv AI

Sep 10

EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation

arXiv:2605.22286v2 Announce Type: replace-cross Abstract: Text-based counseling provides a valuable source of information for assessing depression severity. We study prediction of the total score on...

By Zhaomin Wu, Jiayi Li, Bingsheng He

llms rag benchmarks

arXiv AI

Sep 3

Interpretable Symptom Vectors for Depression in a Large Language Model

The study investigates how a large language model, Gemma-3-27B-PT, internally represents depressive symptoms. By applying mechanistic interpretability methods to the model’s residual stream, researchers found that symptom groups are geometrically distinct at layer 21, and that projected symptom vectors align with clinician-annotated rankings across mood, somatic, and suicidality dimensions. Additionally, a single depression vector at this layer can differentiate depressive from non-depressive text with an AUC of 0.789, suggesting a potential emotional valence gate for symptom projection.

By Fangyi Zhu, Ajay Subramanian, Allison Constant, Camille Wang, Ravish Gupta, Corey J. Keller

llms safety

arXiv Machine Learning

Jul 24

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.

By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao

llms rag multimodal benchmarks safety

arXiv AI

Jun 19

Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language

arXiv:2606. 19640v1 Announce Type: cross Abstract: AI and large language models (LLMs) have emerged as promising tools to address global mental health challenges.

By Yunkai Xu, Saeed Abdullah

llms

arXiv Computation and Language

Sep 10

Multi-Level Narrative Evaluation Outperforms Lexical Features for Mental Health

arXiv:2604.27846v2 Announce Type: replace Abstract: How people narrate their experiences offers a window into how the mind organizes them. Computational approaches to therapeutic writing have evolved...

By Yuxi Ma, Jieming Cui, Muyang Li, Ye Zhao, Yu Li, Yixuan Wang, Chi Zhang, Yinyin Zang, Yixin Zhu

llms rag

arXiv Computation and Language

Sep 3

Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognition

The paper introduces a two‑stage framework for recognizing depression symptoms at the sentence level. First, a contrastively fine‑tuned sentence encoder generates a symptom candidate for each sentence. Then, a fine‑tuned language model verifies the candidate’s presence or absence by comparing the sentence, its context, and a diagnostic definition, ensuring the model’s judgment aligns with that definition before responding.

By Weiming Li, Catarina Barata, Miguel Constante, Joao Sanches

llms fine-tuning safety

arXiv AI

Sep 23

A General Multimodal Probing Framework for Data Fusion and Prediction with Frozen Large Language Models: Evidence from Multimodal EHR Data

arXiv:2606.28798v2 Announce Type: replace Abstract: The main objective of this paper is to propose a general framework for prediction based on different sources of multimodal data in the healthcare d...

By Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen

llms rag fine-tuning multimodal

arXiv AI

Sep 10

PhenoBench: Mapping What a Deeply Phenotyped Human Cohort Can Tell Us

arXiv:2609.06080v1 Announce Type: cross Abstract: Deeply phenotyped cohorts combine clinical, imaging, molecular, and wearable observations across timescales from seconds to years. This breadth can r...

By Gal Sapir, Alon Diament, Adva Wolf, Doron Yaya-Stupp, Dikla Gelbard Solodkin, Dana Azouri, Anat Etzion-Fuchs, Guy Lutsker, Eran Segal, Hagai Rossman

llms multimodal benchmarks

arXiv AI

Oct 7

No Transformer Beats Six Covariates: Long-Horizon Prediction of Depressive Symptoms from Childhood Essays

arXiv:2610.07764v1 Announce Type: cross Abstract: Natural language processing (NLP) models can detect depression-related language in text written near the time symptoms are measured, but whether pret...

By Daniel Kua, Emrul Hasan, John-Jose Nunez, Frances Chen

llms rag nlp fine-tuning

arXiv Computation and Language

Sep 10

False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK

arXiv:2602.13047v2 Announce Type: replace Abstract: Conversational speech reveals early signs of cognitive decline, including dementia and mild cognitive impairment (MCI). AI models show promise for...

By Madhurananda Pahar, Caitlin Illingworth, Dorota Braun, Bahman Mirheidari, Lise Sproson, Daniel Blackburn, Heidi Christensen

agents multimodal safety

arXiv AI

Sep 1

TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment

arXiv:2608.30857v1 Announce Type: cross Abstract: Depression and anxiety in older adults with Mild Cognitive Impairment (MCI) are frequently underdiagnosed due to limited access to care. Multimodal a...

By Merna Bibars, Bolaji Omofojoye, Allan I. Levey, Rachel Hershenberg, Gari D. Clifford, Hyeokhyen Kwon

multimodal safety

arXiv AI

Sep 1

SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LLMs' Robustness to Contradictory Evidence

arXiv:2608.29582v1 Announce Type: cross Abstract: Current evaluations of large language models (LLMs) primarily focus on factual knowledge retrieval, overlooking the fundamental challenge of navigati...

By Yi Yu, Bo Wang, Chong Feng, Ge Shi, Xia Liu, Ziyi Yang, Xuewen Shi

llms benchmarks safety

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