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

• URL: http://arxiv.org/abs/2412.12981v1
• Date: Tue, 17 Dec 2024 15:01:07 GMT
• Title: Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health
• Authors: Vivek Kumar, Eirini Ntoutsi, Pushpraj Singh Rajawat, Giacomo Medda, Diego Reforgiato Recupero,
• Abstract summary: Large language models (LLMs) have shown promising capabilities in healthcare analysis but face several challenges like hallucinations, parroting, and bias manifestation. In this work we introduce IC-AnnoMI, an expert-annotated motivational interviewing (MI) dataset built upon AnnoMI. 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.
• Score: 8.703482957316107
• License:
• Abstract: 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.

Related papers

• REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation [51.97224538045096]
  We introduce REALTALK, a 21-day corpus of authentic messaging app dialogues. We compare EI attributes and persona consistency to understand the challenges posed by real-world dialogues. Our findings reveal that models struggle to simulate a user solely from dialogue history, while fine-tuning on specific user chats improves persona emulation.
  arXiv  Detail & Related papers  (2025-02-18T20:29:01Z)
• KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy [5.305294122910311]
  Motivational Interviewing (MI) is gaining attention as a theoretical basis for boosting expertise in the development of these chatbots. In this paper, we propose a novel framework that simulates MI sessions enriched with the expertise of professional therapists. We present KMI, the first synthetic dataset theoretically grounded in MI, containing 1,000 high-quality Korean Motivational Interviewing dialogues.
  arXiv  Detail & Related papers  (2025-02-08T17:53:41Z)
• Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives [84.03001845263]
  Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Traditional narrative analysis often focuses on local indicators in microstructure, such as word usage and syntax. We propose to investigate specific cognitive and linguistic challenges by analyzing topical shifts, temporal dynamics, and the coherence of narratives over time.
  arXiv  Detail & Related papers  (2025-01-07T12:16:26Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment. We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies [30.237161801912453]
  Large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI) Applying MI strategies, a set of MI skills, to generate more controllable therapeutic-adherent conversations with explainability provides a possible solution.
  arXiv  Detail & Related papers  (2024-08-12T23:19:02Z)
• BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks [2.9873893715462176]
  This work introduces a novel framework named BiosERC, which investigates speaker characteristics in a conversation. By employing Large Language Models (LLMs), we extract the "biographical information" of the speaker within a conversation. Our proposed method achieved state-of-the-art (SOTA) results on three famous benchmark datasets.
  arXiv  Detail & Related papers  (2024-07-05T06:25:34Z)
• Empathy Through Multimodality in Conversational Interfaces [1.360649555639909]
  Conversational Health Agents (CHAs) are redefining healthcare by offering nuanced support that transcends textual analysis to incorporate emotional intelligence. This paper introduces an LLM-based CHA engineered for rich, multimodal dialogue-especially in the realm of mental health support. It adeptly interprets and responds to users' emotional states by analyzing multimodal cues, thus delivering contextually aware and empathetically resonant verbal responses.
  arXiv  Detail & Related papers  (2024-05-08T02:48:29Z)
• GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing [74.68232970965595]
  Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks.
  arXiv  Detail & Related papers  (2024-03-09T13:56:25Z)
• Thread of Thought Unraveling Chaotic Contexts [133.24935874034782]
  "Thread of Thought" (ThoT) strategy draws inspiration from human cognitive processes. In experiments, ThoT significantly improves reasoning performance compared to other prompting techniques.
  arXiv  Detail & Related papers  (2023-11-15T06:54:44Z)
• Affect Recognition in Conversations Using Large Language Models [9.689990547610664]
  Affect recognition plays a pivotal role in human communication. This study investigates the capacity of large language models (LLMs) to recognise human affect in conversations.
  arXiv  Detail & Related papers  (2023-09-22T14:11:23Z)
• Building Emotional Support Chatbots in the Era of LLMs [64.06811786616471]
  We introduce an innovative methodology that synthesizes human insights with the computational prowess of Large Language Models (LLMs) By utilizing the in-context learning potential of ChatGPT, we generate an ExTensible Emotional Support dialogue dataset, named ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions.
  arXiv  Detail & Related papers  (2023-08-17T10:49:18Z)

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.