mlsanityData to 14 Oct 2026

Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health

arXiv 2412.12981 ↗First posted 17 Dec 2024cs.CLcited by 5

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Abstract (arXiv)

Large language models (LLMs) have shown promising capabilities in healthcare analysis but face several challenges like hallucinations, parroting, and bias manifestation. These challenges are exacerbated in complex, sensitive, and low-resource domains. Therefore, in this work we introduce IC-AnnoMI, an expert-annotated motivational interviewing (MI) dataset built upon AnnoMI by generating in-context conversational dialogues leveraging LLMs, particularly ChatGPT. IC-AnnoMI employs targeted prompts accurately engineered through cues and tailored information, taking into account therapy style (empathy, reflection), contextual relevance, and false semantic change. Subsequently, the dialogues are annotated by experts, strictly adhering to the Motivational Interviewing Skills Code (MISC), focusing on both the psychological and linguistic dimensions of MI dialogues. We comprehensively evaluate the IC-AnnoMI dataset and ChatGPT's emotional reasoning ability and understanding of domain intricacies by modeling novel classification tasks employing several classical machine learning and current state-of-the-art transformer approaches. Finally, we discuss the effects of progressive prompting strategies and the impact of augmented data in mitigating the biases manifested in IC-AnnoM. Our contributions provide the MI community with not only a comprehensive dataset but also valuable insights for using LLMs in empathetic text generation for conversational therapy in supervised settings.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 27 of its 84 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

27 of 84 references.

References · 27

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15 more
  1. 2022Selective Annotation Makes Language Models Better Few-Shot Learnerscited by 243
  2. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 199
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Healthcited by 5
  2. 2025Exploring Safety Alignment Evaluation of LLMs in Chinese Mental Health Dialogues via LLM-as-Judgecited by 4
  3. 2025Toward expert-level motivational interviewing for health behavior improvement with LLMscited by 1
  4. 2025Mitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarizationcited by 0
  5. 2026From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Healthcited by 0

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  1. 2025AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewing
  2. 2025LLAMADRS: Evaluating Open-Source LLMs on Real Clinical Interviews--To Reason or Not to Reason?
  3. 2023Towards Interpretable Mental Health Analysis with Large Language Models
  4. 2025Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations
  5. 2026OMIND: Framework for Knowledge Grounded Finetuning and Multi-Turn Dialogue Benchmark for Mental Health LLMs
  6. 2026CALM-IT: Enhancing Synthetic Motivational Interviewing Dialogue Quality with Conversational Dynamics
  7. 2024Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts
  8. 2024Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies