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Language Shapes Mental Health Evaluations in Large Language Models

Jiayi Xu, Xiyang Hu · Mar 6, 2026 · Citations: 0

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Data freshness

Extraction: Fresh

Check recency before relying on this page for active eval decisions. Use stale pages as context and verify against current hub results.

Metadata refreshed

Mar 6, 2026, 10:15 PM

Recent

Extraction refreshed

Mar 14, 2026, 6:13 AM

Fresh

Extraction source

Persisted extraction

Confidence 0.15

Abstract

This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations. Focusing on Chinese and English, we examine two widely used models, GPT-4o and Qwen3, to assess whether prompt language systematically shifts mental health-related evaluations and downstream decision outcomes. First, we assess models' evaluative orientation toward mental health stigma using multiple validated measurement scales capturing social stigma, self-stigma, and professional stigma. Across all measures, both models produce higher stigma-related responses when prompted in Chinese than in English. Second, we examine whether these differences also manifest in two common downstream decision tasks in mental health. In a binary mental health stigma detection task, sensitivity to stigmatizing content varies across language prompts, with lower sensitivity observed under Chinese prompts. In a depression severity classification task, predicted severity also differs by prompt language, with Chinese prompts associated with more underestimation errors, indicating a systematic downward shift in predicted severity relative to English prompts. Together, these findings suggest that language context can systematically shape evaluative patterns in LLM outputs and shift decision thresholds in downstream tasks.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Extraction flags indicate low-signal or possible false-positive protocol mapping.
  • Extraction confidence is 0.15 (below strong-reference threshold).
  • No explicit evaluation mode was extracted from available metadata.
  • No benchmark/dataset or metric anchors were extracted.
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HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit Score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

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Field Provenance & Confidence

Each key protocol field shows extraction state, confidence band, and data source so you can decide whether to trust it directly or validate from full text.

Human Feedback Types

missing

None explicit

Confidence: Low Source: Persisted extraction missing

No explicit feedback protocol extracted.

Evidence snippet: This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.

Evaluation Modes

missing

None explicit

Confidence: Low Source: Persisted extraction missing

Validate eval design from full paper text.

Evidence snippet: This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.

Quality Controls

missing

Not reported

Confidence: Low Source: Persisted extraction missing

No explicit QC controls found.

Evidence snippet: This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No benchmark anchors detected.

Evidence snippet: This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.

Reported Metrics

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No metric anchors detected.

Evidence snippet: This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.

Rater Population

missing

Unknown

Confidence: Low Source: Persisted extraction missing

Rater source not explicitly reported.

Evidence snippet: This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Unknown
  • Expertise required: General
  • Extraction source: Persisted extraction

Evaluation Lens

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Confidence: 0.15
  • Flags: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Deterministic synthesis

This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations. HFEPX protocol signal is limited in abstract-level metadata, so treat it as adjacent context. Updated from current HFEPX corpus.

Generated Mar 14, 2026, 6:13 AM · Grounded in abstract + metadata only

Key Takeaways

  • This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.
  • Focusing on Chinese and English, we examine two widely used models, GPT-4o and Qwen3, to assess whether prompt language systematically shifts mental health-related evaluations and…

Researcher Actions

  • Treat this as method context, then pivot to protocol-specific HFEPX hubs.
  • Identify benchmark choices from full text before operationalizing conclusions.
  • Verify metric definitions before comparing against your eval pipeline.

Caveats

  • Generated from title, abstract, and extracted metadata only; full-paper implementation details are not parsed.
  • Low-signal flag detected: protocol relevance may be indirect.

Research Summary

Contribution Summary

  • This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.
  • Focusing on Chinese and English, we examine two widely used models, GPT-4o and Qwen3, to assess whether prompt language systematically shifts mental health-related evaluations and downstream decision outcomes.

Why It Matters For Eval

  • This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations.
  • Focusing on Chinese and English, we examine two widely used models, GPT-4o and Qwen3, to assess whether prompt language systematically shifts mental health-related evaluations and downstream decision outcomes.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

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