Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts

• URL: http://arxiv.org/abs/2608.29446v1
• Date: Sat, 29 Aug 2026 21:23:57 GMT
• Title: Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts
• Abstract summary: Large language models (LLMs) used for distress detection are typically aligned to a single, undifferentiated standard.<n>How well do these models capture the perspectives of the communities whose language they assess?<n>We evaluate nine open-weight LLM configurations and four frontier configurations against these labels.
• Score: 4.801119915366712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Judgments about psychological distress are socially situated: what counts as concerning hinges on community norms around emotional expression, vulnerability, and help-seeking. Yet large language models (LLMs) used for distress detection are typically aligned to a single, undifferentiated standard. How well do these models capture the perspectives of the communities whose language they assess? We address this question through a perspectivist annotation study in which 321 participants provided 9,587 judgments on 1,198 Reddit posts spanning six identity-based communities, yielding community-specific labels. Raters in the contextualized in-group condition show a modest tendency to agree more with their community than uncontextualized out-group raters (OR = 1.18), an effect varying significantly across communities. We then evaluate nine open-weight LLM configurations and four frontier configurations against these labels. Open-weight LLMs systematically over-estimate distress: when communities perceive none-to-mild distress, these models achieve only 31-44% accuracy, predominantly producing false positives. GPT-5 and Gemini 2.5 Pro show the same none-to-mild inflation even when their full-sample over/under rates are mixed, while Claude Opus 4 is more conservative. This pattern does not simply mirror an outsider reading position: uncontextualized out-group human aggregates were nearly symmetric, with 18% over-estimation versus 19% under-estimation. Instead, the models that inflate none-to-mild cases exhibit a distress prior that exceeds both contextualized in-group and uncontextualized out-group human judgments. These findings have implications for equitable AI deployment in mental health contexts, where miscalibrated distress detection may unevenly affect the communities being assessed.

Related papers

• SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue [23.532923483545076]
  We introduce SDARE-Bench, the first scenario-based benchmark evaluating stigma detection and open-ended response generation in large language models.<n> Empirical results show poor identification of stigma components, especially in group dialogues.<n>Our findings identify stigma response as a recurring LLM safety vulnerability, especially in socially complex conversational contexts.
  arXiv  Detail & Related papers  (2026-09-01T17:15:22Z)
• To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias [61.40435736418359]
  We introduce a unified framework that standardizes heterogeneous benchmarks to contrast isolated demographic assessments with forced-choice comparative settings.<n>We show that comparative settings act as aggressive catalysts for latent discrimination, a shift primarily driven by underspecified contexts.<n>Finally, we demonstrate that this comparative prejudice is a generalized phenomenon that scales positively with model size.
  arXiv  Detail & Related papers  (2026-06-23T13:53:50Z)
• Human-like in-group bias in instruction-tuned language model agents [0.33842793760651557]
  Group-contingent targeting as a property of instruction-tuned language models.<n>Per-interaction targeting propagates into structural inequality in persistent networks.
  arXiv  Detail & Related papers  (2026-05-27T08:06:50Z)
• The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission [0.0]
  We show that GPT-4o, Claude, and Gemini exhibit highly correlated forecasting errors on binary prediction questions.<n>In Study 2, we test whether this correlated bias has propagated into human crowd forecasts.<n>In Study 3, we examine whether the category-level pattern of human forecasting errors increasingly resembles the LLM bias fingerprint.
  arXiv  Detail & Related papers  (2026-04-07T19:45:40Z)
• Evaluating Alignment of Behavioral Dispositions in LLMs [15.282965130762648]
  We focus on behavioral dispositions that shape responses in social contexts.<n>We introduce a framework to study how closely the dispositions expressed by LLMs align with those of humans.
  arXiv  Detail & Related papers  (2026-02-11T19:59:12Z)
• Us-vs-Them bias in Large Language Models [0.569978892646475]
  We find consistent ingroup-positive and outgroup-negative associations across foundational large language models.<n>For personas examined, conservative personas exhibit greater outgroup hostility, whereas liberal personas display stronger ingroup solidarity.
  arXiv  Detail & Related papers  (2025-12-03T07:11:22Z)
• Evaluating & Reducing Deceptive Dialogue From Language Models with Multi-turn RL [64.3268313484078]
  Large Language Models (LLMs) interact with millions of people worldwide in applications such as customer support, education and healthcare.<n>Their ability to produce deceptive outputs, whether intentionally or inadvertently, poses significant safety concerns.<n>We investigate the extent to which LLMs engage in deception within dialogue, and propose the belief misalignment metric to quantify deception.
  arXiv  Detail & Related papers  (2025-10-16T05:29:36Z)
• Fairness Mediator: Neutralize Stereotype Associations to Mitigate Bias in Large Language Models [66.5536396328527]
  LLMs inadvertently absorb spurious correlations from training data, leading to stereotype associations between biased concepts and specific social groups.<n>We propose Fairness Mediator (FairMed), a bias mitigation framework that neutralizes stereotype associations.<n>Our framework comprises two main components: a stereotype association prober and an adversarial debiasing neutralizer.
  arXiv  Detail & Related papers  (2025-04-10T14:23:06Z)
• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models [91.86718720024825]
  We center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias.<n>Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning.<n>We conclude with recommendations tailored to DPO and broader alignment practices.
  arXiv  Detail & Related papers  (2024-11-06T06:50:50Z)
• Diverging Preferences: When do Annotators Disagree and do Models Know? [92.24651142187989]
  We develop a taxonomy of disagreement sources spanning 10 categories across four high-level classes. We find that the majority of disagreements are in opposition with standard reward modeling approaches. We develop methods for identifying diverging preferences to mitigate their influence on evaluation and training.
  arXiv  Detail & Related papers  (2024-10-18T17:32:22Z)
• Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective [66.34066553400108]
  We conduct a rigorous evaluation of large language models' implicit bias towards certain demographics.<n>Inspired by psychometric principles, we propose three attack approaches, i.e., Disguise, Deception, and Teaching.<n>Our methods can elicit LLMs' inner bias more effectively than competitive baselines.
  arXiv  Detail & Related papers  (2024-06-20T06:42:08Z)
• Bias Against 93 Stigmatized Groups in Masked Language Models and Downstream Sentiment Classification Tasks [2.5690340428649323]
  This study extends the focus of bias evaluation in extant work by examining bias against social stigmas on a large scale. It focuses on 93 stigmatized groups in the United States, including a wide range of conditions related to disease, disability, drug use, mental illness, religion, sexuality, socioeconomic status, and other relevant factors. We investigate bias against these groups in English pre-trained Masked Language Models (MLMs) and their downstream sentiment classification tasks.
  arXiv  Detail & Related papers  (2023-06-08T20:46:09Z)

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.