SMILE : Single-turn to Multi-turn Inclusive Language Expansion via C hat GPT for Mental Health Support (2024)

Abstract: Developing specialized dialogue systems for mental health support requires multi-turn conversation data, which has recently garnered increasing attention. However, gathering and releasing large-scale, real-life multi-turn conversations that could facilitate advancements in mental health support presents challenges in data privacy protection and the time and cost involved in crowdsourcing. To address these challenges, we introduce SMILE, a single-turn to multi-turn inclusive language expansion technique that prompts ChatGPT to rewrite public single-turn dialogues into multi-turn ones. Our work begins by analyzing language transformation and validating the feasibility of our proposed method. We conduct a study on dialogue diversity, including lexical features, semantic features, and dialogue topics, demonstrating the effectiveness of our method. Further, we employ our method to generate a large-scale, lifelike, and diverse dialogue dataset named SMILECHAT, consisting of 55k dialogues. Finally, we utilize the collected corpus to develop a mental health chatbot, MeChat. To better assess the quality of SMILECHAT, we collect a small-scale real-life counseling dataset conducted by data anonymization. Both automatic and human evaluations demonstrate significant improvements in our dialogue system and confirm that SMILECHAT is high-quality. Code, data, and model are publicly available at https://github.com/qiuhuachuan/smile.

## introduction we all know the importance of mental health, and mental health issues have been a persistent concern for human beings @xcite . recent advancements in natural language processing (nlp) technology @xcite have led to the emergence of neural-based conversational ai in mental health support @xcite . as an innovative solution for mental health, virtual counselors powered by large language models (llms) can effectively address accessibility barriers, such as the high cost of treatment and the shortage of experienced professionals to meet the demand. furthermore, such dialogue systems provide mental health support as an effective and practical online counseling approach for those in need, safeguarding user privacy and mitigating the stigma that often accompanies helpseeking. however, the lack of publicly available, large-scale, diverse, and high-quality multi-turn chat datasets in the mental health support domain hinders the development of specialized dialogue systems. motivation indeed, many researchers have been pursuing the goal of building a practical and effective conversational agent for mental health. however, the first step in creating such a system is to have training data. conversations related to mental health support often contain sensitive information and must be kept confidential to safeguard the privacy of individuals seeking help @xcite . making these conversations publicly available may discourage individuals from seeking support or negatively impact their personal and professional lives once known to people with whom they are acquainted. to facilitate progress in the nlp community, some researchers have attempted to collect various dialogue corpora @xcite @xcite through crowdsourcing, data crawling, or data augmentation to build a dialogue agent capable of providing emotional and mental health support. how to construct a large-scale, diverse, and high-quality multi-turn chat dataset for mental health without effort motivates us to carry out the work as presented in this paper. challenges to be more specific, crowdsourcing conversations @xcite for emotional support has limitations due to the high cost and time required to train and manage annotators, as well as the difficulty in mimicking real-life interactions, that is, interlocutors may lack an understanding of the dilemma of living with mental disorders. an alternative is crawling qa @xcite on a public mental health forum for training psychological support models. however, single-turn conversations may not be sufficient for resolving mental health issues, as multiple interaction exchanges are often needed. multi-turn conversations, which can better simulate real-world conversations, are more practical for training psychological support models. while the post-triggered machine-augmented method @xcite can address the limitations of scale and topic diversity, it does not consider the responses of experienced supporters. our approach to tackle the abovementioned challenges, we introduce smile, single-turn to multi-turn inclusive language expansion via chat-gpt, as shown in figure 1 . specifically, we instruct chatgpt to transform publicly available long question-answer pairs (public qas), which can also be viewed as single-turn dialogues, into multi-turn conversations. with the proposed method, we build a large-scale, diverse, and high-quality multi-turn conversation dataset for mental health support. our paper is organized as follows: ## method psyqa foot_1 , an open-source and high-quality chinese single-turn dialogue dataset focused on mental health support, features one question mapped to multiple answers. our dataset creation pipeline, based on psyqa, includes three main stages: (1) data preparation, (2) task definition, and (3) prompt design. ## dialogue diversity to demonstrate the effectiveness of the smile method, we mainly focus on three aspects of diversity: lexical features, semantic features, and dialogue topics. ## dialogue system we aim to build a high-quality multi-turn chat dataset for mental health support. it is non-trivial to evaluate the quality of a dialogue dataset, which is often assessed indirectly by the dialogue system. therefore, we need to train a dialogue system and analyze its performance. ## conclusion this paper introduces smile, a simple yet effective solution for addressing the data scarcity of multi-turn conversations in mental health support. through language transformation and diversity analysis, we confirm the feasibility and effectiveness of our approach. the proposed method automatically creates a large-scale, lifelike, diverse, and high-quality dialogue corpus, smilechat, consisting of 55165 dialogues with an average of 5.7 turns. both automatic and human evaluations using the psytest dataset, consisting of 50 reallife anonymized counseling dialogues, demonstrate that smilechat significantly improves dialogue system performance in mental health support. we release multi-turn dialogues (smilechat), a dia-logue model (mechat), and a real-life anonymized test set (psytest) to drive progress in the research community.