Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversations

• URL: http://arxiv.org/abs/2406.08718v1
• Date: Thu, 13 Jun 2024 00:48:44 GMT
• Title: Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversations
• Authors: Jun-Woo Kim, Ji-Eun Han, Jun-Seok Koh, Hyeon-Tae Seo, Du-Seong Chang,
• Abstract summary: We introduce a pipeline that leverages Large Language Models (LLMs) to transform single-turn psychotherapy counseling sessions into multi-turn interactions. Our approach significantly enhances the ability of LLMs to produce higher quality multi-turn dialogues in the context of mental health counseling.
• Score: 1.0286011319699866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a pipeline that leverages Large Language Models (LLMs) to transform single-turn psychotherapy counseling sessions into multi-turn interactions. While AI-supported online counseling services for individuals with mental disorders exist, they are often constrained by the limited availability of multi-turn training datasets and frequently fail to fully utilize therapists' expertise. Our proposed pipeline effectively addresses these limitations. The pipeline comprises two main steps: 1) Information Extraction and 2) Multi-turn Counseling Generation. Each step is meticulously designed to extract and generate comprehensive multi-turn counseling conversations from the available datasets. Experimental results from both zero-shot and few-shot generation scenarios demonstrate that our approach significantly enhances the ability of LLMs to produce higher quality multi-turn dialogues in the context of mental health counseling. Our pipeline and dataset are publicly available https://github.com/jwkim-chat/A-Data-Augmentation-Pipeline-Leveraging-Large-Language-Models-for-Coun seling-Conversations.

Related papers

• Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models [0.8563446809549775]
  Mental health has become a global issue that reveals the limitations of traditional conversational psychotherapy. We introduce SoulSpeak, a Large Language Model (LLM)-enabled chatbots designed to democratize access to psychotherapy.
  arXiv  Detail & Related papers  (2024-12-04T03:02:46Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Optimizing Psychological Counseling with Instruction-Tuned Large Language Models [9.19192059750618]
  This paper explores the application of large language models (LLMs) in psychological counseling. We present a method for instruction tuning LLMs with specialized prompts to enhance their performance in providing empathetic, relevant, and supportive responses.
  arXiv  Detail & Related papers  (2024-06-19T15:13:07Z)
• Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models [17.236038165057817]
  We leverage a decision transformer architecture for topic recommendation in counseling conversations. The architecture is utilized for offline reinforcement learning. We propose a novel system of utilizing our model's output as synthetic labels for fine-tuning a large language model.
  arXiv  Detail & Related papers  (2024-05-08T13:55:25Z)
• Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support [1.9336815376402723]
  Large Language Models (LLMs) have demonstrated remarkable performance across various information-seeking and reasoning tasks. This study sought to examine LLMs' capability to generate empathetic responses in conversations that emulate those in a mental health counselling setting. We selected five LLMs: version 3.5 and version 4 of the Generative Pre-training (GPT), Vicuna FastChat-T5, Pathways Language Model (PaLM) version 2, and Falcon-7B-Instruct.
  arXiv  Detail & Related papers  (2023-10-12T03:33:06Z)
• 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)
• Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
  We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
  arXiv  Detail & Related papers  (2023-05-24T14:06:27Z)
• Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling [25.524804770124145]
  We present READER, a dialogue-act guided response generator for mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act d(t+1) for the next utterance (aka response-act) and to generate an appropriate response u(t+1) We evaluate READER on HOPE, a benchmark counseling conversation dataset.
  arXiv  Detail & Related papers  (2023-01-30T08:53:35Z)
• GDPR Compliant Collection of Therapist-Patient-Dialogues [48.091760741427656]
  We elaborate on the challenges we faced in starting our collection of therapist-patient dialogues in a psychiatry clinic under the General Data Privacy Regulation of the European Union. We give an overview of each step in our procedure and point out the potential pitfalls to motivate further research in this field.
  arXiv  Detail & Related papers  (2022-11-22T15:51:10Z)
• Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue System [120.70726465994781]
  multimodal spoken dialogue system enables telephonebased agents to interact with customers like human. We deploy Conversation Duplex Alibaba intelligent customer service to share lessons learned in production. Online A/B experiments show in proposed system can significantly reduce response latency by 50%.
  arXiv  Detail & Related papers  (2022-05-30T12:41:23Z)
• Smoothing Dialogue States for Open Conversational Machine Reading [70.83783364292438]
  We propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation. Experiments on the OR-ShARC dataset show the effectiveness of our method, which achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-08-28T08:04:28Z)

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.