Unlocking LLM s: Addressing Scarce Data and Bias Challenges in Mental Health and Therapeutic Counselling (2024)

Abstract: 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.

## introduction motivational interviewing (mi) is a client-centered, directive method of conversational counselling that enhances an individual's motivation to achieve behavioural change @xcite . mi helps the clients resolve ambivalence and focus on intrinsic motivations by "strengthening client's belief in their capability" or "providing a supportive environment" to make positive changes @xcite @xcite . mi has gained wide attention from the clinical psychology community due to its proven efficacy in catalyzing significant improvements in health behaviours such as reducing alcohol consumption, smoking cessation, dietary modification, substance abuse, and increasing physical activity @xcite @xcite . in particular, mi have been very effective in interventions where client adherence and commitment are critical to successful treatment outcomes @xcite . in a nutshell, the core principles of mi, namely, "expressing empathy", "developing discrepancy", "rolling with resistance", and "supporting self-efficacy", are designed to promote a non-confrontational approach that respects client autonomy and facilitates self-directed change @xcite . since mi is an interactive and time-intensive process, it is accessible to only a small population group, and the reasons account for "individual's awareness towards mental health", "cost of intervention", "lifestyle constraints", and so on. according to world health organization report foot_0 , one in every eight people in the world live with a mental disorder and over half (54.7%) of adults with a mental condition do not have access to effective treatment, summing up over 28 million individuals @xcite . hence, to overcome these challenges and break the barriers in catering to essential and effective treatment, recent research has focused on artificial intelligence (ai) applications. in particular, large language models (llms) have been recognised as a potential solution to alleviate the burden on clinicians @xcite @xcite . undoubtedly, llms can be instrumental in tackling a wide range of problems directly or by means of assisting roles @xcite . however, due to its specialised nature, the mental health domain poses unique challenges of complex language understanding that question llms efficacy @xcite . empirical studies have delineated that in such complex domains, llms are prone to severe performance concerns like hallucinations @xcite , stochastic parroting nature @xcite , and biases @xcite . therefore, this study aims to bridge this gap by addressing the scarce data and bias challenges in low-resource domains, such as mental health, by generating plausible synthetic data. in this context, we leverage llms, particularly chatgpt and novel prompting strategies, to generate in-context @xcite @xcite mi dialogues, considering whole therapeutic conversations at once. furthermore, we develop an evaluation scheme adhering to the manual for the motivational interviewing skill code (misc) @xcite to assess the quality of generated mi dialogues by comprehensively touching down the psychological and linguistic dimensions. moreover, we model a novel classification task to identify high-and low-quality mi dialogues. this setting is used to evaluate chatgpt in terms of domain intricacies understanding, emotional reasoning ability, and biases (contextual, sampling, class imbalance) originated from the experimental dataset. finally, we discuss the risks of unsupervised employment of llms in healthcare, emphasizing the need for collaboration with domain experts and human supervision to ensure responsible llm implementation across healthcare settings. to put in perspective, our contributions are summarised below: we publicly foot_1 provide ic-annomi and the source code used for our experiments to contribute to the low resource domain and facilitate further research. the rest of the paper is organised as follows. section 2 presents the existing research on llms in healthcare. section 3 presents the data augmentation, misc annotation, and the dataset creation. section 4 provides the problem statement and experimental design. section 5 outlines our experimental setting and results. section 6 addresses the implications of our study and opens up future research directions. finally, the limitations section discusses the limitations of our work. ## related work in this section, we introduce the works focused on developing public datasets to assist research into psychology and highlight the biases affecting llms. ## data augmentation, misc annotation and dataset creation in this section, we describe (i) the data augmentation strategy, (ii) how the misc annotation scheme is developed, and (iii) how the annotation scheme was used to create the dataset. for ease of understanding, ## problem statement and experimental design this section presents the problem statement and the research questions we aimed to answer through this research, followed by the dataset description, the applied preprocessing strategies, and the evaluation setup to conduct the experiments. ## result and discussion in this section, we provide insights from our results and in-depth analyses based on our experimental outcomes. the classification results of the implemented ml models with the non-augmented and augmented ic-annomi datasets are summed up in note that the applied augmentation method is not centered on reducing the class imbalance in the experimental dataset by targeting the minority class, which is low-quality mi in our case, but on preserving the context of each dialogue. therefore, this augmentation is not expected to contribute significantly to applied ml models' performance, but to have more of an impact on increasing the sample size of the training set. the main experimental observations are as follows: the statistics of expert annotated components of m i psych and m i linguist of m i syn. are also in agreement with the above performance, which strengthens our results. for instance, m i psych has received an average score of 3.31 for the 97 m i syn. averaged over its five attributes and then averaged over 97 m i syn. . also, for the m i linguist aspect of 97 m i syn. , 95.88% have preserved the context, 83.51% have contributed to text enrichment, mi enhancement is observed in 88,66% and overall m i lang is 88,66%. this paper explores llms' capabilities, particularly chatgpt, for data augmentation in mental health and therapeutic counselling scenarios. through this research, we seek to study the operability of llms in solving the data scarcity issue in therapeutic counselling and verify that biases are not reinforced when models are trained on llm-generated synthetic data. to this end, we employed a progressive prompt technique to generate in-context plausible mi dialogues and further expert annotated them by developing a comprehensive misc coding scheme considering mi sessions' psychological and linguistic aspects. to evaluate the quality of generated mi dialogues and to understand to what extent the generated dataset is relevant to the annotation scheme, we employed several cml and transformer-based models to establish a baseline for the classification task of mi dialogues' quality at the utterance level. our results