N-shot prompting is an NLP technique for guiding LLMs to perform specific tasks with “N” examples in the prompt to understand the task. It enables in-context learning of LLMs for better performance with minimal additional training (2). Based on the in-context (“N”) examples provided to LLMs, n-shot prompting can be further separated into zero-shot prompting and few-shot prompting. Zero-shot prompting has shown some promising results in performing well-designed prompt-driven non-complex tasks such as information retrieval, language translations, and question answering, without corresponding task-specific examples where the model must rely on its pre-existing knowledge and the task description in the prompt (25). Recent studies such as (13) and Lamichhane (14) have shown the capability of zero-shot prompting in ChatGPT for depression and suicidal detection. Few-shot prompting, on the other hand, performs well in complex tasks such as custom text generation and domain-specific question answering, using in-context examples (typically between two and five) along with task-specific prompts to steer an LLM for better understanding the task and to produce more accurate and contextually appropriate responses, compared to zero-shot prompting (26). Mental-RoBERTa (27) and Mental-FLAN-T5 (15) have been used to classify depression, stress, and suicidal thoughts using few-shot prompting.

COT prompting is an NLP technique to improve the reasoning capabilities of LLMs using structured prompts and immediate reasoning steps. In contrast with the application of LLMs to classification tasks using N-shot prompting, COT prompting helps the LLM to breakdown complex problems into manageable tasks and improves its ability to handle tasks using multi-step problem solving and explanation generation (28). Assessing the accuracy of LLM-generated explanations for mental health is critical. Kojima et al. (29) modified the vanilla prompt design using COT prompting to enhance the reasoning capability of GPT-3.5 and GPT-4 in metal health contexts. Englhardt et al. (30) suggested a novel approach based on multi-model time-series data to improve the reasoning abilities of LLMs for detecting depression and anxiety. A few studies have shown the explainability of LLMs in the context of mental health using end-user applications such as chatbots (31). Wang et al. (32) proposed a new COT framework to assess the mental status of users following multiple COT prompting reasoning steps in both zero-shot and few-shot settings. Chen et al. (33) introduced an enhanced version of COT prompting called Diagnosis of Thought prompting, a conceptual approach similar to COT prompting but focused more on understanding and validating the thought process behind the LLM's responses, to detect cognitive distortions. Although COT prompting improves the LLM's ability to handle complex tasks compared to N-shot prompting, the quality of prompts can limit the effectiveness.

ICL is the simplest PE method to adapt the knowledge of GPT-3 to solve a new, semantically similar tasks without additional explicit training using in-context examples, also known as demonstrations, inspired by the knowledge transferability of the human brain to new tasks using few instructions (2). Liu et al. (34) showed the importance of dynamically retrieved demonstrations over random demonstrations for natural language generation (NLG) tasks. Hayati et al. (35) explored the few-shot capability of GPT-3 for depression detection using contextually similar demonstrations. Su et al. (36) further demonstrated the mental health reasoning capabilities of LLMs using a new ICL framework. Fu et al. (37) introduced a commonsense-based response generation method by enhancing the explainability of ChatGPT and T5 models in the context of mental health using domain-specific demonstrations. Recently (38), developed the GoodTimes app, a personalized conversational and storytelling tool for reminiscence therapy, using the ICL-based reasoning capabilities of SOTA NLP models. As shown in Table 1, ICL-based N-shot prompting shows significant results in depression, stress, and suicidal thought detection (14, 15). Even though multiple DMH studies have been conducted for contextually similar knowledge transfer using ICL-based techniques, adapting knowledge to contextually dissimilar tasks is yet to be achieved due to limitations such as the lack of relevant contextual cues, differences in dissimilar tasks structures, limited generalization of LLMs to transfer knowledge, and the complexity of creating effective prompts for contextually dissimilar tasks (39).

Considering the limitations of ICL, soft continuous prompts were proposed to enhance the in-context capability of GPT-3 to execute a new task by adapting a few parameters while keeping the majority of the LLM frozen (4). Blair et al. (40) introduced a few-shot PT-based domain transfer technique for named entity disambiguation in mental health news articles. Li et al. (41) suggested novel PT-based optimization methods and a reinforcement learning framework for GPT-4 which can be used for mental health related NLG tasks. Spathis et al. (42) used PT-based evaluation protocols such as zero-shot inference to work with temporal stress levels data. According to Table 1, PT-based N-shot prompting performs better than ICL-based N-shot and COT prompting in suicidal thoughts and cognitive distortion detection (11, 12). PT-based methods are still unstable for scaling LLMs even though such methods outperform ICL-based approaches due to optimization challenges, LLM complexity, and the absence of sufficient contextual information for LLM generalization (43).

Recently, IPT was introduced as a combination of ICT and PT to facilitate the knowledge transfer of contextually dissimilar tasks by concatenating soft continuous prompts of the source task with retrieved demonstrations of the target task (39). Singhal et al. (44) introduced the concept of an LLMs' transferability to unseen tasks in classification and NLG medical domains. Nguyen et al. (45) proposed a novel depression screening process based on out-of-domain knowledge transfer methods. Ji (10) introduced an NLP-based suicidal risk detection method based on the sentiment classification capability of LLMs. Gupta et al. (46) explored the LLMs' zero-shot performance on unseen dialogue-related NLG tasks and cross-task generalization in multiple dialogue settings. The same approach was further modified to enhance the cross-task generalization capability of GPT-3 on stress screening (47).

Anxiety detection, depression detection, and suicidality detection are the most cited application domains of the DMH classification task. Abd-Alrazaq et al. (31) were the first to present a scoping review for n-shot ICL-based prompt engineering techniques in DMH. EMU framework, compatible with passive modalities, was introduced to screen depression and anxiety and the corpus was made publicly available for research purposes (9). Amin et al. (13) analyzed the depression detection capability of ChatGPT using n-shot prompting. Yang et al. (12) explored mental health analysis across five tasks including depression classification and introduced a reliable annotation protocol using emotion-enhanced COT prompting. Mental-LLM was introduced as a SOTA LLM for depression and stress classification using GPT-3.5 and GPT-4 prompting (15). Qi et al. (11) showed suicidality detection in social media posts using zero-shot and few-shot ICL-based prompting. Guo et al. (16) invented a topic modelling framework for depression detection on low-resource data based on handcrafted n-shot prompting. Only a few studies focused on the quality of generated responses by ChatGPT for suicidality detection using n-shot prompting (17). Recently (20), investigated LLM prompting for DMH using large and noisy social media corpora.

Considering the reasoning capabilities of LLMs, several generation-based tasks for DMH can be identified. Prompt-based generation is important to predict mental health conditions. Yang et al. (12) showed the sensitivity of LLMs for different input prompts such as severe and very severe in explainable mental health analysis while mitigating the consequences using few-shot prompting. LLaMA-2 was used as a text augmentation assistant in content generation for mental healthcare treatment planning (50). MentalLLaMA was invented to improve the interpretability of LLMs in DMH (18). Qin et al. (19) introduced a novel COT prompting approach for depression detection and reasoning using zero-shot and few-shot out-of-distribution, which are unseen samples within the same domain, settings. Recently, this was further enhanced using explainable LLM-based techniques to understand psychological state (22). Agrawal (23) improved the explainability and reasoning of the latest generative LLMs in depression analysis using a novel COT prompting framework. Inspired by the Generate, Annotate, and Learn (GAL) framework by (51), a novel suicidality detection framework was introduced to generate synthetic data using LLMs to improve explainability (52). In comparison with classification-based tasks, most of the generation-based tasks use COT prompting as the PE type.

Only a few recent studies have demonstrated question-answering in psychological consultation services and online counselling for mental health professionals. Frameworks such as Psy-LLM, pretrained with LLMs and prompt-tuned with question-answering from psychologists, provide peer support and mental health advice in psychological consultation (53). Liu et al. (54) presented ChatCounselor, an enhanced LLM-based chatbot fine-tuned with domain-specific prompts and demonstrations to reinforce high-quality reasoning and question-answering in DMH. Recently (24), introduced BOLT, an ICL-based framework, to characterize the conversational behavior of clients and therapists.