Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs

• URL: http://arxiv.org/abs/2606.23387v2
• Date: Thu, 25 Jun 2026 19:43:12 GMT
• Title: Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs
• Abstract summary: Self-stigma predicts treatment avoidance and disengagement among people who use drugs.<n>We present a three-phase, proof-of-concept study of a persona-aware approach to LLM support.<n>Our findings suggest that holistic empathy judgments and clinically-aligned response design can pull in opposite directions.
• Score: 2.815883047237096
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Self-stigma predicts treatment avoidance and disengagement among people who use drugs (PWUD), yet conversational systems aiming to provide support typically treat self-stigma expression as a uniform signal. We present a three-phase, proof-of-concept study of a persona-aware approach to LLM support. Latent Profile Analysis (LPA) on indicator-level features from 1,174 self-stigma expressors on Reddit yields a four-persona typology validated against held-out behavioral and linguistic features. Sequential Bayesian and recurrent neural classifiers recover these personas from limited posting histories, substantially outperforming batch and few-shot LLM baselines (macro-F1 = 0.74 at 30 posts). Evaluation by eight clinical experts across three contemporary LLMs revealed a misalignment: persona-matched responses successfully achieved targeted behavioral shifts, yet raters holistically preferred the generic empathy of the persona-neutral baseline. Our findings suggest that holistic empathy judgments and clinically-aligned response design can pull in opposite directions, and that evaluating LLM-based stigma support requires rubrics capable of decomposing the two.

Related papers

• Empathy Applicability Modeling for General Health Queries [16.390464387095175]
  We introduce the Empathy Applicability Framework (EAF), a theory-driven approach that classifies patient queries in terms of the applicability of emotional reactions and interpretations.<n>EAF provides a framework for identifying empathy needs before response generation, establishes a benchmark for anticipatory empathy modeling.
  arXiv  Detail & Related papers  (2026-01-14T18:47:02Z)
• Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models [48.95516224614331]
  We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation.<n>Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical, and implicit adherence to safety protocols.
  arXiv  Detail & Related papers  (2026-01-11T02:20:40Z)
• Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning [52.07170679746533]
  Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play.<n>We introduce a unified framework for evaluating and improving persona consistency in LLM-generated dialogue.<n>We define three automatic metrics: prompt-to-line consistency, line-to-line consistency, and Q&A consistency, that capture different types of persona drift and validate each against human annotations.
  arXiv  Detail & Related papers  (2025-10-31T19:40:41Z)
• IROTE: Human-like Traits Elicitation of Large Language Model via In-Context Self-Reflective Optimization [66.6349183886101]
  We propose IROTE, a novel in-context method for stable and transferable trait elicitation.<n>We show that one single IROTE-generated self-reflection can induce LLMs' stable impersonation of the target trait across diverse downstream tasks.
  arXiv  Detail & Related papers  (2025-08-12T08:04:28Z)
• Mental Health Equity in LLMs: Leveraging Multi-Hop Question Answering to Detect Amplified and Silenced Perspectives [9.24608617206594]
  Large Language Models (LLMs) in mental healthcare risk propagating biases that reinforce stigma and harm marginalized groups.<n>This work introduces a multi-hop question answering framework to explore LLM response biases in mental health discourse.<n>Using systematic tagging across age, race, gender, and socioeconomic status, we investigate bias patterns at demographic intersections.
  arXiv  Detail & Related papers  (2025-06-22T18:00:16Z)
• Arbiters of Ambivalence: Challenges of Using LLMs in No-Consensus Tasks [52.098988739649705]
  This study examines the biases and limitations of LLMs in three roles: answer generator, judge, and debater.<n>We develop a no-consensus'' benchmark by curating examples that encompass a variety of a priori ambivalent scenarios.<n>Our results show that while LLMs can provide nuanced assessments when generating open-ended answers, they tend to take a stance on no-consensus topics when employed as judges or debaters.
  arXiv  Detail & Related papers  (2025-05-28T01:31:54Z)
• Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation [7.867257950096845]
  Plain language summaries (PLSs) are essential for facilitating effective communication between clinicians and patients.<n>Large language models (LLMs) have recently shown promise in automating PLS generation, but their effectiveness in supporting health information comprehension remains unclear.<n>We conducted a large-scale crowdsourced evaluation of LLM-generated PLSs using Amazon Mechanical Turk with 150 participants.<n>Our findings indicate that while LLMs can generate PLSs that appear indistinguishable from human-written ones in subjective evaluations, human-written PLSs lead to significantly better comprehension.
  arXiv  Detail & Related papers  (2025-05-15T15:31:17Z)
• Understanding the Dark Side of LLMs' Intrinsic Self-Correction [58.12627172032851]
  Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability.<n>Recent works show that LLMs' intrinsic self-correction fails without oracle labels as feedback prompts.<n>We identify intrinsic self-correction can cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions.
  arXiv  Detail & Related papers  (2024-12-19T15:39:31Z)
• Can AI Relate: Testing Large Language Model Response for Mental Health Support [23.97212082563385]
  Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. We develop an evaluation framework for determining whether LLM response is a viable and ethical path forward for the automation of mental health treatment.
  arXiv  Detail & Related papers  (2024-05-20T13:42:27Z)
• Limited Ability of LLMs to Simulate Human Psychological Behaviours: a Psychometric Analysis [0.27309692684728604]
  We prompt OpenAI's flagship models, GPT-3.5 and GPT-4, to assume different personas and respond to a range of standardized measures of personality constructs. We found that the responses from GPT-4, but not GPT-3.5, using generic persona descriptions show promising, albeit not perfect, psychometric properties.
  arXiv  Detail & Related papers  (2024-05-12T10:52:15Z)
• Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
  This study aims to determine the reliability of applying personality assessments to Large Language Models. Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
  arXiv  Detail & Related papers  (2023-05-31T15:03:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.