Venue: NAACL 2025

# KMI: A Dataset of Korean Motivational

Interviewing Dialogues for Psychotherapy

Hyunjong Kim\({}^{1}\)  Suyeon Lee\({}^{1*}\)   Yeongjae Cho\({}^{1*}\)

**Eunseo Ryu\({}^{1}\)****Yohan Jo\({}^{1}\)****Suran Seong\({}^{2}\)****Sungaoon Cho\({}^{1\dagger}\)**

\({}^{1}\)Seoul National University   \({}^{2}\)Korea Counseling Graduate University

{hjkim, suyeon.lee, yjcho}@bdai.snu.ac.kr

{eunseoryu, yohan.jo, zoon}@snu.ac.kr

nina9698@gmail.com

Equal contribution.Corresponding author.

###### Abstract

The increasing demand for mental health services has led to the rise of AI-driven mental health chatbots, though challenges related to privacy, data collection, and expertise persist. Motivational Interviewing (MI) is gaining attention as a theoretical basis for boosting expertise in the development of these chatbots. However, existing datasets are showing limitations for training chatbots, leading to a substantial demand for publicly available resources in the field of MI and psychotherapy. These challenges are even more pronounced in non-English languages, where they receive less attention. In this paper, we propose a novel framework that simulates MI sessions enriched with the expertise of professional therapists. We train an MI forecaster model that mimics the behavioral choices of professional therapists and employ Large Language Models (LLMs) to generate utterances through prompt engineering. Then, we present **KMI**, the first synthetic dataset theoretically grounded in MI, containing 1,000 high-quality **K**orean **M**ovtiational **I**nterivering dialogues. Through an extensive expert evaluation of the generated dataset and the dialogue model trained on it, we demonstrate the quality, expertise, and practicality of KMI. We also introduce novel metrics derived from MI theory in order to evaluate dialogues from the perspective of MI.

## 1 Introduction

In modern society, the issue of mental health is emerging as a critical problem, with an increasing demand for mental health services. This has led to a shortage of mental health workers available to meet the growing demand Butryn et al. (2017). Thus, there has been considerable expansion in the investigation of AI-driven chatbots providing mental health assistance Inkster et al. (2018); Mousavi et al. (2021). However, there are many challenges in training such chatbots. It suffers from privacy issues, requires a considerable amount of cost and time for collecting data, and crucially, needs expertise in mental health. Cho et al. (2023) pointed out that many mental health chatbots are designed without any underlying theory for psychotherapy, casting doubt on their utility for mental health support.

As a theoretical basis for enhancing expertise, Motivational Interviewing (MI) has been attracting increasing attention in developing mental health support chatbots Park et al. (2019); He et al. (2022); Brown et al. (2023). MI is a client-centered counseling technique to elicit behavior change by helping clients explore and resolve ambivalence Miller and Rollnick (2002). MI has been an active area of research intersecting the disciplines of psychotherapy and Natural Language Processing (NLP), due to its effectiveness and well-structured behavioral coding systems such as MITI (Motivational Interviewing Treatment Integrity) Moyers et al. (2003, 2014).

Few works have proposed MI datasets with annotations from MI behavioral coding systems. Perez

Figure 1: Comparison between KMI and existing resources. KMI is the only dataset that is a non-English resource, based on MI theory, and not derived from transcripts. Other datasets fall short in some of these aspects, limiting their suitability for chatbot applications.



Rosas et al. (2016) developed an MI dataset using automatic captioning of YouTube and Vimeo videos. However, the dataset is currently not publicly available due to ethical reasons. Welivita and Pu (2022) proposed an alternative of scraping data from online peer support forums. Although these dialogues are available in abundance, they have shown significant differences from those collected from professional counselors in terms of MI, thus lacking expertise. In line with this problem, Wu et al. (2023) presented _AnmoMI_, a dataset of 133 MI conversations that were professionally transcribed from MI demonstration videos and further annotated by experienced MI practitioners. Despite its decent transcription and annotation quality, it significantly lacks in quantity due to the limited video sources and the intensive human labor required.

In short, the mentioned datasets contain limitations to be used for training MI-boosted chatbots, such as lack of expertise or insufficient quantity. Also, transcripts often include many onomatopoeic words and instances of stammering, which are undesirable in chatbots. Furthermore, previous works have predominantly focused on English. While increasing language coverage is crucial in addressing the challenges of mental health support Cho et al. (2023), research on other languages and cultures has been relatively understudied.

To address these issues, we propose a novel framework to generate synthetic dialogues that simulate MI sessions using Large Language Models (LLMs). We carefully design two agents, the therapist simulator and the client simulator, which alternately take turns to generate utterances based on few-shot in-context learning. To incorporate the expertise of professional therapists, we train an MI forecaster model, which predicts the next-turn therapist behavior. Also, we focus on a non-English language, Korean in our case1, and ground the dialogues in real-world contexts that reflect actual Korean circumstances. Consequently, we present **KMI**, a dataset of 1,000 **K**orean **M**otivational **I**nterviewing dialogues. As illustrated in Figure 1, KMI is the only dataset that overcomes the previously mentioned limitations of existing resources. KMI covers a wide range of concerns and auxieties common among Koreans, while successfully integrating the MI strategy of professional therapists simultaneously. Each therapist utterance within the dataset is annotated with a therapist behavior label. To the best of our knowledge, our work is the first attempt to construct an MI dataset using the generative capability of LLMs.

Footnote 1: South Korea is struggling with widespread mental health problems such as depression and anxiety, highlighted by the highest suicide rate among OECD countries OECD (2023).

We evaluate KMI through a comprehensive evaluation process by engaging professional counselors. We first validate the dataset's quality from the perspective of both MI and general dialogue systems. Notably, for the MI-driven evaluation, we introduce novel metrics derived from MI theory to directly measure how well the conversation complies with the spirit of MI. Then, we train a dialogue model using our dataset to assess its utility as a resource for training a chatbot for mental health support. Evaluation results demonstrate that our dataset not only effectively captures the essence of MI, but also offers practical utility for chatbot development.

Our contributions are as follows.

1. We propose a novel framework that simulates MI sessions enriched with the expertise of professional therapists. This is achieved by training and leveraging an MI forecaster model that mimics the behavioral choices of real-world therapists.
2. We present KMI, the first synthetic dataset theoretically grounded in MI, which contains 1,000 high-quality Korean MI dialogues. We publicly release our dataset to address the issue of data shortage in psychotherapy2. Footnote 2: [https://github.com/hjkim811/KMI](https://github.com/hjkim811/KMI)
3. Through an extensive expert evaluation of the generated dataset and the dialogue model trained on it, we demonstrate the quality, expertise, and practicality of KMI. We also introduce novel metrics derived from MI theory in order to evaluate dialogues from the perspective of MI.

## 2 Related Work

Dialogue Generation Using LLMsRecent research has increasingly focused on using LLMs to generate dialogues for various applications. Kim et al. (2022) proposed a human-machine collaborative framework to build a large-scale dialogue dataset for training conversational agents to handle problematic content appropriately. Chen et al. (2023a) used a small number of expert-written conversations as in-context examples to create synthetic multi-party conversations. Chen et al.

 

(2023b) proposed LLM prompting methods to generate mixed-initiative dialogues. Kim et al. (2023) created a large-scale social dialogue dataset by distilling conversations from LLMs. Macina et al. (2023) paired human teachers with an LLM student to generate teacher-student tutoring dialogues grounded in math reasoning problems.

NLP Applications in MIMI was developed as a technique to assist individuals in resolving ambivalence and committing to change Miller (1983), representing an evolution of client-centered therapy. Along with the advancement in NLP, considerable research efforts have been underway to apply NLP techniques in the field of MI. Several studies have been proposed to automatically classify a given utterance into one of the MI behavioral codes. While earlier approaches utilize linguistic features Perez-Rosas et al. (2017) and recurrent neural network architectures Tanana et al. (2015); Xiao et al. (2016); Cao et al. (2019); Gibson et al. (2019), recent approaches make use of pre-trained language models such as RoBERTa Liu et al. (2019); Tavabi et al. (2021); Welivita and Pu (2023). Some works adopt a multimodal approach, leveraging additional information such as speech features Tavabi et al. (2020) or facial features Nakano et al. (2022).

Welivita and Pu (2023) demonstrated the potential of LLMs in boosting dialogues using the MI strategy. They observed that among the MI dataset curated from online platforms Welivita and Pu (2022), 92.86% of the advice given by peers falls into the _Advise without permission_ category, which is MI non-adherent. To make the dataset more MI-consistent, they fine-tuned BlenderBot Roller et al. (2021) and GPT-3 Brown et al. (2020) to rephrase these responses into more MI-adherent _Advise with permission_ responses. Although this work demonstrated the possibility of leveraging LLMs in MI, the impact of rephrasing remains marginal, considering that its capability is restricted to revising the manner of speech and has limitations in modifying the content itself. In our work, we further exploit the generative ability of LLMs to generate the entire dataset from scratch.

## 3 KMI: A Dataset of Korean Motivational Interviewing Dialogues

This section introduces a novel framework to generate realistic motivational interviewing dialogues. The overall framework is illustrated in Figure 2. First, we collect context data (Section 3.1) that reflects the actual concerns and anxieties experienced by Koreans. Each context serves as the topic of each dialogue. Next, we simulate an MI session (Section 3.2) using a therapist simulator and client simulator. The resulting KMI dataset is presented in Section 3.3.

### Collecting Context Data

To construct context data for generating realistic dialogues, we web-crawl posts from a Korean psychological counseling platform _Mindcafe3_, which contain common concerns of Koreans. We collect a

Figure 2: The overall framework for generating the KMI dataset. The context data and dialogue history are originally in Korean but have been translated into English for the figure.

 

total of 7,530 posts from seven categories: mental health, interpersonal relationships, ego & personality, career & employment, academic & examination, addiction & obsession, and family. Then, using GPT-4 API4(Achiam et al., 2023), we score these posts on a scale from 1 to 3 in terms of the post's specificity and its suitability as a topic for an MI session. Among the 3,098 posts that received a score of 3, a total of 1,000 posts were sampled with a predetermined quantity for each category. Details regarding the collection, filtering, and sampling of context data can be found in Appendix A.

Footnote 4: gpt-4-@125-preview

### Motivational Interviewing

For each context, we simulate an MI session using an LLM-based therapist simulator (Section 3.2.1) and client simulator (Section 3.2.2). They alternately take turns to generate utterances. The conversation begins with a general open question by the therapist, such as 'Hello, what concerns have brought you here today?". For both simulators, we utilize GPT-4 API for high-quality generation.

We define eight labels derived from MITI code 2.0 (Moyers et al., 2003) and 4.2.1 (Moyers et al., 2014) to categorize therapist behaviors. Table 1 shows the description and example of each label. Each therapist utterance in the final KMI dataset is annotated with one of these eight labels, which helps capture the nuances of therapeutic conversations and enhances the dataset's utility for various NLP applications.

#### 3.2.1 Therapist Simulator

Using MI-consistent techniques and blending them skillfully helps motivate clients to change (Moyers et al., 2014). Thus, it is crucial to follow the behavioral choices of professional therapists to generate high-quality MI dialogues. In this work, this is achieved by first predicting the next-turn therapist behavior via MI forecaster and decision module, and then generating the utterance based on the predicted behavior by prompting an LLM. If one could simulate a realistic therapist with expertise, LLVM-based simulation could be a significant breakthrough in the field of MI, alleviating the issues of extensive human workload and privacy concerns.

MI ForecasterMI forecaster aims to predict the next-turn therapist behavior, which is one of the MI labels listed in Table 1, given a dialogue history. We fine-tune T5-base (Raffel et al., 2020) with a converted dataset of AnnoMI (Wu et al., 2023), which is preprocessed and converted for a forecasting task. AnnoMI is an expert-annotated MI dialogue dataset. As the original dataset doesn't include _Affirm_ among its utterance labels, we add _Affirm_ label to the dataset using the RoBERTa-based MI classifier5 developed in Welivita and Pu (2023). More details regarding the preprocessing of AnnoMI can be found in Appendix B.

Footnote 5: The checkpoints can be found in [https://github.com/anuradha192/Boosting-with-MI-Strategy](https://github.com/anuradha192/Boosting-with-MI-Strategy).

Then we construct pairs of input text and output text, where input text is the dialogue history prefixed with the task instruction (Predictnext therapist's dialogue act) and output text is the next-turn therapist behavior label. We explore various modeling settings to find the model with the best predictive power: (1) we test history window sizes from 1 to 8 and (2) try inserting the MI behavior label of each therapist utterance in the dialogue history to provide additional information to the model. An examp

\begin{table}
\begin{tabular}{l l p{160.7pt}} \hline \hline
**MH Label** & **Description** & **Examples** \\ \hline
1. Simple Reflection & Repetition, rephrasing, or paraphrasing of the speaker’s previous statement. & It sounds like you’re feeling worried. \\
2. Complex Reflection & Repeating or rephrasing the previous statement of the speaker but adding substantial meaning/emphas to it. & _Speaker_: Mostly, I would change for future generations. \\
3. Open Question & Questions that allow a wide range of possible answers. & What is your take on that? \\
4. Closed Question & Questions that can be answered with a yescho response or a very restricted range of answers. & Do you think this is an advantage? \\
5. Affirm & Encouraging the speaker by saying something positive or complimentary. & You should be pooud of yourself for your past efforts. \\
6. Give Information & Educating, providing feedback, or giving an opinion without advising. & Logging your cravings is important as carvings often lead to relapses. \\
7. Advise & Making suggestions, offering solutions or possible actions. & We could try to brainstorm some ideas that might help. \\
8. Other & Statements that are not classified under the above codes. & Hi there. \\ \hline \hline  & & \\ \end{tabular}
\end{table}
Table 1: MI labels derived from the MITI code. The descriptions and examples of each label are taken from Welivita and Pu (2022).

 

shown in Table 12 of Appendix C. To form the input text, we concatenate the utterances in dialogue history, each prefixed with a special token indicating the speaker. In the setting where therapist labels are provided, the annotated label of every therapist utterance in dialogue history is inserted in the special token.

Considering the relatively small size of the dataset, we use 5-fold cross-validation to evaluate each model setting. Statistics of the dataset and implementation details can be found in Appendix C. Though the next-turn label from AnnoMI is used as the ground-truth label, it is not the only correct choice given the nature of the task; even in the same situation, different therapists might opt for different strategies and approaches to deal with the client depending on their counseling style and therapeutic philosophy. Therefore, we use top-3 accuracy instead of top-1 accuracy when monitoring the model performance, assuming that the response is plausible if the model's prediction matches the label in the dataset within three attempts.

The experimental results are illustrated in Figure 3. We observe that the model performs best when six utterances are given as dialogue history and therapist labels are provided, demonstrating 71.26% top-3 accuracy. Also, inserting therapist labels in the dialogue history has improved accuracy and reduced cross-fold variance, offering benefits in the forecasting task. To verify the performance of the MI forecaster, we provide additional baselines _Majority_ and _Random_. Majority predicts the three most frequent labels for top-3 prediction, while Random predicts labels randomly. Our model significantly outperforms these baselines, validating its effectiveness.

Finally, we train the final MI forecaster model for the generation process of KMI. Based on the experimental results, we adopt the optimal setting previously mentioned and use all available data for training, as a held-out test set is no longer required at this stage. In each therapist's turn, the MI forecaster predicts the three most likely MI labels based on the previously generated dialogue history. Since the generated utterances that function as input text are in Korean, we first translate the dialogue history into English and then concatenate six recent utterances to form the input text. We utilize Upstage SOLAR Kim et al. (2024) translation API6 for translation.

Footnote 6: solar-l-mini-translate-koen model

Decision ModuleIn each therapist's turn, among the three labels the MI forecaster predicted, the decision module decides the final therapist label to generate. Based on two simple rules, it complements the MI forecaster from a broader perspective: (1) The same therapist label cannot appear three times in a row. (2) Either open or closed, the therapist cannot ask questions three times in a row. The first rule is devised to keep the dialogue from being too homogeneous. The second rule is a direct implementation of a clinical guideline that advises against asking three questions in a row Miller and Rollnick (2002). Such behavior might direct the client into a passive, question-answering role, which should be avoided in MI. In the order of top-1, top-2, and top-3 predictions, the decision module checks each label against these two rules. The first prediction that complies with both rules is determined to be the final therapist's behavior for the next turn.

Utterance GenerationOnce the label is determined, we generate the therapist's utterance by prompting an LLM to generate an utterance based on the determined label and dialogue history. We leverage in-context learning for this purpose, providing the definition and three examples of the corresponding label. The definition and examples are excerpted from Korean MI textbooks Schumacher and Madson (2017); Shin and Cho (2016); Shin (2019), which could be considered among the most credible sources available. These examples contain speech patterns and linguistic expressions actually used by professional Korean counselors. In all prompts, we specify predefined constraints to generate consistent and natural utterances. Also,

Figure 3: Top-3 accuracy of the MI forecaster using 5-fold cross-validation. The bold line represents the mean accuracy across folds and the shaded area indicates the 95% confidence interval.

 

the simulator is instructed to conclude the conversation if the client's concerns seem to have been resolved. The prompt for generating a simple reflection utterance, which includes instructions, constraints, definitions, and examples of simple reflection, and dialogue history is shown in Table 14 of Appendix E. Along with the generated utterances, the corresponding MI labels for each utterance are included in our dataset as utterance-level annotations.

#### 3.2.2 Client Simulator

We generate client utterances by directly prompting an LLM. We provide the context and instruct the LLM to generate responses based on the context and dialogue history. In order to depict the evolving states of clients throughout the MI session, we adopt a simple but effective approach to instruct the LLM to generate _change talk_, a client language that indicates movement toward a particular change Miller and Rollnick (2023), if interaction with the counselor inspired a willingness to change and speaking about change appears suitable within the dialogue context. As in the therapist simulator, we provide the definition and four examples of change talk excerpted from Korean MI textbooks Schumacher and Madson (2017); Shin and Cho (2016); Shin (2019) for in-context learning. The four examples each include one from the four types of preparatory change talk--desire, ability, reasons, and need (often abbreviated as DARN) Miller and Rollnick (2023)--encompassing various types of change talk. Descriptions of each type can be found in Appendix D. Also, we specify predefined constraints in each prompt. The prompt for generating a client utterance, which includes instructions, constraints, definition, and examples of change talk, context, and dialogue history is shown in Table 15 of Appendix E. Based on the proposed framework, we finally generate KMI, a dataset consisting of 1,000 Korean MI dialogues.

### Statistics

As shown in Table 2, KMI consists of 1,000 long-turn dialogues, with an average of 18.12 turns per dialogue. Each utterance by therapists is assigned one of the MI labels. As detailed in Table 2, _Complex Reflection_ emerges as the most frequent label within KMI, accounting for 36% of all therapist utterances, followed by _Open Question_ and _Simple Reflection_. Although there are no strict guidelines for the proportion of MI labels, MITI coding manual Moyers et al. (2014) provides summary scores to measure clinicians' competence in using MI. A Reflection-to-Question Ratio (R:Q) of 1:1 is considered 'fair', while 2:1 is considered 'good'. KMI yields a ratio of 1.8:1, implying that it meets the standard of professional clinicians. An example dialogue in KMI is illustrated in Figure 4.

## 4 Evaluation

We evaluate the quality of the generated dataset (Section 4.2) and the dialogue model fine-tuned with it (Section 4.3). We compare our dataset with CounselGPT7, the only Korean counseling dataset to date that is created using OpenAI API, and AnnoMI. We translate AnnoMI into Korean using Up

Figure 4: An example of KMI dataset. The dialogue has been translated into English for the figure. Examples of full dialogues are provided in Appendix J.

 

stage SOLAR (Kim et al., 2024) translation API8, in order to evaluate it in the context of Korean.

Footnote 7: [https://github.com/tml-mini-transform-in-dataset-enzo-model](https://github.com/tml-mini-transform-in-dataset-enzo-model)

### Evaluation Criteria

We evaluate the datasets and dialogue models from two perspectives: MI and general dialogue systems.

MI QualityWe aim to measure how closely the dialogues adhere to the principles of MI. To achieve this goal, we propose novel metrics derived from MI theory: (1) **Partnership**, (2) **Acceptance**, (3) **Compassion**, (4) **Evocation**, (5) **Similarity**, and (6) **Effectiveness**. (1)-(4) are derived from the fundamental spirit of MI (Miller and Rollnick, 2012). Similarity measures how closely the generated therapist's utterances resemble those of an actual therapist. Effectiveness measures the overall efficacy of the MI session. Descriptions of each evaluation criterion can be found in Appendix F.

General QualityFollowing previous works (Wan et al., 2022; Chen et al., 2023a), we use three evaluation criteria to measure the general quality of the dialogues: (1) **Consistency**, (2) **Fluency**, and (3) **On-topic**. Consistency examines whether the entire dialogue and utterances between turns are consistent. Fluency assesses the flow of the dialogue and the naturalness and fluidity of each utterance. On-topic evaluates whether the dialogue is relevant to the provided context.

### Evaluation of Dataset

Evaluation FrameworkWe assess the MI quality and general quality of KMI based on human evaluation, particularly by experts. We recruited four Korean psychological counseling experts9 experienced in MI. For evaluation, we randomly sample 100 dialogues from each of KMI, CounselGPT, and AnnoMI. Sampling details of KMI are available in Appendix H.1. The experts are then requested to evaluate the dialogues based on the criteria defined in Section 4.1, using a Likert scale ranging from 1 to 5. The median score is calculated by applying the majority vote approach. The evaluation form we used is shown in Figure 5 of Appendix I.

Footnote 9: [http://www.kmi.org/](http://www.kmi.org/)

MI QualityAs demonstrated in Table 3, KMI surpasses CounselGPT and AnnoMI across all evaluation criteria of MI quality. CounselGPT, which is not built on MI theory, shows mostly low scores across the MI quality criteria. AnnoMI achieves high scores on the MI quality criteria but shows a lower similarity score than our dataset. This might be due to the quality degradation that occurs during translation, as a subtle difference in nuance could be significant in the context of psychotherapy. Finally, achieving a score of 3.94 in similarity and 3.74 in effectiveness illustrates that our dataset qualifies as a valuable psychotherapeutic resource.

General QualityAs shown in Table 3, KMI exhibits a high score of 4.65 in consistency and 4.22 in fluency, both of which outperform CounselGPT and AnnoMI. This indicates that our framework, tailored to Korean, capably generates high-quality dialogues. Also, the on-topic score of 4.17 demonstrates that the dialogues in KMI showcase a strong alignment with actual Korean situations. As CounselGPT and AnnoMI do not contain context data, on-topic is not evaluated on these datasets.

MI Label AccuracyIn addition to the qualitative evaluation, we measure the accuracy of MI labels in the dataset to validate its reliability. We randomly sample 30 utterances for each MI label from KMI, resulting in a total of 210 utterances10. Sampling details are available in Appendix H.2. We then ask the experts to assess whether each utterance matches its assigned label, using True/False evaluation. Results in Table 4 demonstrate that most of the utterances are consistent with their label, with an average accuracy of 96.0%.

\begin{table}
\begin{tabular}{l r r r} \hline \hline
**Dataset** & KMI & CounselGPT & AnnoMI \\ \hline
**MI Quality** & & & \\ Partnership & **4.40** & 2.78 & 4.03 \\ Acceptance & **4.35** & 2.86 & 3.99 \\ Compassion & **3.81** & 3.05 & 3.50 \\ Evocation & **4.07** & 2.88 & 3.87 \\ Similarity & **3.94** & 2.96 & 3.72 \\ Effectiveness & **3.74** & 2.66 & 3.45 \\ \hline

- \\ \hline \hline \end{tabular}
\end{table}
Table 3: Human evaluation results of MI quality and general quality for sampled dialogues from KMI, CounselGPT, and AnnoMI. All differences in pairwise comparisons between KMI and the other two datasets are statistically significant at a significance level of 0.01.
 



### Evaluation of Dialogue Model

Training Dialogue ModelsWe assess dialogue models fine-tuned with these datasets to verify the utility of KMI as a training dataset for chatbots. We fine-tune komt-llama2-7b-v11, an LLaMA 2-7B (Touvron et al., 2023) model further tuned with Korean multi-task instruction tuning, using each of KMI, CounselGPT, and translated AnnoMI. Fine-tuning details are stated in Appendix G.

Footnote 11: [https://github.com/davidkim205/komt](https://github.com/davidkim205/komt)

Evaluation FrameworkWe recruited four native Korean crowdworkers to participate in conversations with the dialogue models. Given context data, they are asked to interactively converse with the model as if they were the writer of the given context. They engage in 30 conversations with each dialogue model, using an identical set of 30 contexts for each model. Among the collected data, context data not previously used for data generation is used in this step. The completed dialogues are then evaluated by experts12 based on the aforementioned criteria, using a 5-point Likert scale.

Footnote 12: The same experts as in Section 4.2.

MI QualityResults in Table 5 show that the dialogue model fine-tuned on KMI outperforms other models across all MI quality criteria by a substantial margin. This highlights the value of KMI for building mental health chatbots, especially those specialized in MI. We speculate that the relatively low scores for similarity and effectiveness are related to the capability of the base model, komt-llama2-7b-v1, since it might be challenging for a 7B-sized model to proficiently role-play a professional therapist or lead an effective MI session from a professional perspective. The scores of CounselGPT and AnnoMI are mostly below 3, implying limitations in their suitability for training MI chatbots.

As shown in dialogue examples from each model in Appendix K, the model fine-tuned with KMI generates utterances that comply with the principles of MI. In contrast, models fine-tuned with CounselGPT and AnnoMI lack the features of MI and tend to produce excessively lengthy utterances.

General QualityResults in Table 5 also show that the dialogue model fine-tuned on KMI demonstrates better consistency and fluency than other models. AnnoMI displays the lowest scores in these metrics, indicating the limitations of using a transcribed dataset for chatbot development. On-topic is not evaluated in dialogue models because it's the user's role to raise and discuss content related to the context data, not the model's.

## 5 Discussion and Conclusion

In this paper, we introduced a novel framework to generate synthetic motivational interviewing dialogues, along with KMI, the resulting dataset of 1,000 Korean MI dialogues. Through comprehensive evaluations, we demonstrated its quality and utility for chatbot development. For meaningful evaluation, we also proposed novel metrics derived from MI theory.

Our dataset has three main applications. First, it could be used for developing mental health chatbots grounded in MI theory. We trained a Korean chatbot in this paper, where expert evaluation results and usage examples (see Table 22) show that our dataset is capable of building effective chatbots.

\begin{table}
\begin{tabular}{l r} \hline \hline
**MI Label** & **Accuracy (\%)** \\ \hline Simple Reflection & 96.7 \\ Complex Reflection & 96.7 \\ Open Question & 100.0 \\ Closed Question & 95.0 \\ Affirm & 96.7 \\ Give Information & 90.0 \\ Advise & 96.7 \\ \hline Average & **96.0** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of the correspondence between utterances and MI labels.

\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dialogue Model** & KMI & CounselGPT & AnnoMI \\ \hline
**MI Quality** & & & \\ Partnership & **4.43** & 2.97 & 3.17 \\ Acceptance & **4.04** & 2.69 & 2.74 \\ Compassion & **3.29** & 2.31 & 2.44 \\ Evocation & **3.70** & 2.92 & 2.73 \\ Similarity & **3.18** & 2.60 & 2.46 \\ Effectiveness & **3.00** & 2.10 & 2.28 \\ \hline
**General Quality** & & & \\ Consistency & **4.30** & 3.70 & 3.40 \\ Fluency & **3.55** & 2.70 & 2.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Human evaluation results of MI quality and general quality for dialogue models fine-tuned using KMI, CounselGPT, and AnnoMI. All differences in pairwise comparisons between KMI and the other two datasets are statistically significant at a significance level of 0.01.



Second, it could function as a labeled dataset of (Utterance, MI label) pairs for classification and forecasting tasks. MI label accuracy of 96.0% demonstrates the reliability of the labels. Third, it could serve as a reference for MI practitioners. Full dialogues of therapy sessions are usually private, while MI textbooks provide only segments of dialogues. Despite being synthetic, KMI can offer high-quality, full dialogues covering diverse topics.

In addition, our generation method is potentially generalizable to other languages. Korean-specific resources required in our framework are: (1) context data, (2) 3-4 high-quality examples for each MI label for few-shot prompting, and (3) a Korean-to-English translation model. If these resources are available, our framework could be further used to boost non-English resources for psychotherapy.

### Limitations

Error AnalysisWhile our framework generally performs well, we found some cases where the dialogue doesn't sufficiently reflect the contents and details of the context data. These cases are discussed below:

(1) When the client simulator generates an utterance, it considers both the context and dialogue history. The ideal scenario is when it responds to the therapist while incorporating the details of the context at the same time, but sometimes the counselor might lead the conversation in a direction that doesn't align with the context data. In such cases, the client simulator primarily focuses on responding to the therapist's last utterance, potentially resulting in somewhat shallow or meandering dialogues that don't contain the details of the context data.

(2) As the context data is collected from an online counseling platform, some of it contains extreme or violent content. We found that GPT-4 automatically filters such content when generating utterances, removing problematic elements from the final KMI dataset. Dialogues generated based on such context might be less specific and realistic.

MI Forecaster Trained on English DatasetAs there were no public Korean MI resources available prior to our work, we used AnnoMI, an English MI dialogue dataset, to train the MI forecaster. AnnoMI consists entirely of English dialogues, most of which are based on Western cultures. Thus, the predictions made by the MI forecaster might be biased toward the practices of therapists from these cultural backgrounds. However, we suppose that the core principles of MI such as partnership, acceptance, compassion, and evocation remain universal regardless of the language and culture. It is known that MI is well-suited to working with different languages and culturally diverse populations Miller and Rollnick (2023). Also, McMaster and Griva (2015) discovered that over 50 groups of health professionals across North America, Europe, Africa, and Asia generated remarkably similar responses when asked what they would consider good practice and bad practice in their own settings. This indicates that good MI practice would still remain good MI practice in different cultures, supporting the validity of our approach.

Effect of TranslationTwo kinds of translations are involved in our research. First, in the generation process of KMI, we translate the generated dialogue history into English to use it as the input for the MI forecaster. Second, we translate AnnoMI into Korean during the evaluation phase13. In both cases, we utilized Upstage SOLAR Kim et al. (2024), one of the most recent translation models, to preserve the meaning of sentences, but some degradation is inevitable. We consider the latter more critical since slight variations in nuance or expression can have a substantial impact in the context of psychotherapy, possibly resulting in lower scores for AnnoMI. We believe the effect of translation is more trivial in the former case, as the MI forecaster considers the overall content and flow of the dialogue history to make predictions, rather than focusing on detailed expressions.

Footnote 13: Since KMI is the first Korean dataset grounded in MI, we had to choose an MI dataset from a different language for comparison.

### Ethics Statement

Dataset CurationIn the Korean psychological counseling platform _Mindcafe_, individuals are anonymous and they recognize that their writing is archived on the site unless they delete it. We collected only publicly available data and there was no interaction with the Mindcafe users.

Human EvaluationFor human evaluation, we recruited four professional counselors. They were compensated with 50,000 won per hour. For the evaluation of dialogue models, we also recruited four crowdworkers. They were compensated with 10,000 won per hour, which is higher than the Korean minimum wage at the time they worked.

 

Mental Health Support ChatbotsThe KMI dataset was designed to facilitate the advancement of chatbots that adhere to MI principles, particularly for mental health support. However, despite the promising capabilities of recent AI models, AI-driven chatbots may still pose risks. The unpredictability of generative models can lead to unintended consequences, especially in sensitive conversations involving emotional distress. Therefore, any deployment of chatbots trained on KMI should be approached with caution, and human supervision is essential to ensure that the responses are appropriate.

## Acknowledgments

This work was supported by the BK21 FOUR Program (Education and Research Center for Industrial Innovation Analytics) funded by the Ministry of Education, Korea (No. 4120240214912) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2093785, RS-2024-00333484, RS-2024-00414981).

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Context Data Details

We collect a total of 7,530 posts in seven categories from Mindcafe. Table 6 shows the number of collected data by category and the number of data that received 3-point scores. The number and ratio of data by score are presented in Table 7. Scoring is conducted using few-shot prompting with GPT-4 API, and the prompt used is shown in Table 9. The filtered context data are then randomly sampled with predetermined quantities for each category and used for data generation. The number of data sampled for each category is detailed in Table 8.

**Instruction:**

Below is an utterance from a client who has come for counseling with a therapist. Based on the provided definition of Motivational Interviewing (MI), the characteristics of topics suitable for MI, examples of appropriate and inappropriate utterances, and the classification criteria, please classify whether the utterance below is appropriate or not. Do not explain the reason, just answer with a number only.

**Definition of MI:**

It is a cooperative and goal-oriented conversation method that strengthens an individual's own motivation and commitment to change. Set within an accepting and empathetic atmosphere, it encourages individuals to explore and articulate their own reasons for change, thereby bolstering their personal motivation and commitment toward a specific goal.

**Characteristics of Topics Suitable for MI:**

- Cases where an individual wants to change but struggles due to ambivalence about the change or lack of motivation.

- Problems that can be somewhat improved if the individual decides to make an effort on their own.

**Example of Appropriate Utterance:**

Lately, I've been having these thoughts. My heart is complex, and my mind is troubled, making me feel exhausted lately and like I've lost the vitality I used to have. I just don't seem to have any energy, I feel lethargic, and everything seems to be my fault. It's really hard. I'm very afraid, and simply living feels exhausting. These days, I just want to take sleeping pills and sleep for two days without thinking about anything. Why is this happening? What should I do in times like these?

**Example of Inappropriate Utterance:**

I've been having a lot of strange dreams. In my dreams, a woman in red clothes tries to kill me with a knife, and things like that. I've actually seen ghosts and heard their voices. It really feels like someone is constantly watching me... My grandmother says that young people shouldn't go to shams and that it's not an option for me. I'm really at a loss; I'm so scared even now, feeling restless.... I don't want to go to a psychiatist, and I can't go to a shaman... What should I do?

**Scoring Criteria:**

- 3 points: The content is specific, and the topic is suitable for MI.

- 2 points: The content is specific, but the topic is not suitable for MI.

- 1 point: The content is not specific or has some flaw; counseling about someone other than oneself (family, friends, etc.) scores I point

**Utreatence:**

I'm not sure where things went wrong, but my life seems to have been fraught with problems right from the start. Interacting with others, including my family, and even dealing with myself, is a challenge. Starting anything feels like a monumental task; where others glide through effortlessly, I find myself having to exert many times more effort just to keep up. I'm always having to plan several steps ahead for anything I do, and navigating relationships and society feels like an extension of his perpetual struggle. Everything feels so hard. Giving up has become a part of my daily routine. How can I turn my situation around?

\begin{table}
\begin{tabular}{l r r} \hline \hline
**Category** & **\# Data** & **\# Filtered Data** \\ \hline Mental Health & 2,651 & 1,102 \\ Interpersonal Relationships & 1,338 & 535 \\ Ego \& Personality & 1,300 & 538 \\ Career \& Employment & 874 & 477 \\ Academic \& Examination & 645 & 247 \\ Family & 527 & 126 \\ Addiction \& Obsession & 195 & 73 \\ \hline Total & 7,530 & 3,098 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of data collected by category and filtered by a score of 3 within each category.

\begin{table}
\begin{tabular}{l c} \hline \hline
**Score** & **\# Data** & **\%** \\ \hline
3 & 3,603 & 47.9 \\
2 & 3,729 & 49.5 \\
1 & 198 & 2.6 \\ \hline Total & 7,530 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number and percentage of data by score.
 

Preprocessing AnnoMI

We follow the steps below to preprocess the AnnoMI dataset for our task.

(1) As we want to simulate high-quality MI sessions, we only use the dialogues annotated \(\mathtt{high}\text{-}\mathsf{quality}\) in the original dataset, which leaves 110 dialogues out of 133.

(2) For therapist utterances that have several MI labels, we only leave the one annotated as \(\mathtt{main}\_\mathtt{therapist}\_\mathtt{behaviour}\) in the original dataset, so that each therapist utterance has only one label.

(3) For the six MI labels shown in Table 10 that correspond one-to-one in AnnoMI and our taxonomy, we directly use them with slight modifications in the label names.

(4) For the therapist utterances not categorized as one of the labels in Table 10, we further classify them using the RoBERTa-based MI classifier developed in Welivita and Pu (2023). We add _A/firm_ label to the utterances the classifier predicts as _Affirm_.

(5) The remaining therapist utterances are labeled as _Other_.

## Appendix C Details of MI Forecaster

We show the number of converted data for training the MI forecaster according to its history window size in Table 11. If the input text exceeds the maximum token length of 512, we truncate it from the left side to preserve the recent history. An example of the converted data is shown in Table 12.

Hyperparameters for training the MI forecaster are listed in Table 13. We use AdamW Loshchilov and Hutter, 2017 for optimization.

 

Change Talk

Change talk is a client language that indicates movement toward a particular change (Miller and Rollnick, 2023). Four types of change talk--desire, ability, reasons, and need (often abbreviated as DARN)--are called preparatory change talk because you hear them when people are considering whether to do something. Descriptions and examples of each type are stated below.

* **Desire language** is a way of saying, "I want." It signals some inclination toward action. ("I want to quit smoking.")
* **Ability language** provides information about how confident people are that they would be able to take the action in question. ("I think it's possible for me to quit.")
* **Reason language** states specific reasons for doing something. The reason might be a possible advantage of change or a disadvantage of not changing. ("My children are begging me to quit.")
* **Need language** has an imperative quality emphasizing some urgency of change. It implies that a change is important without specifying why. ("I've got to quit smoking.") 



## Appendix E Prompt Templates

**Instruction:**

```
1.50in \(\mathbb{R}^{3}\)[\(\alpha_{i}\), \(\beta_{j}\)] = \(\alpha_{i}\), \(\beta_{j}\), \(\gamma_{ij}\), \(\delta_{ij}\), \(\



## Appendix A

\begin{table} \end{table}
Table 15: Prompt template for client utterance generation.



Evaluation Criteria

MI QualityIn our work, the following criteria are used to evaluate MI quality:

* **Partnership** assesses whether the therapist respects the client's expertise, defined as the client's experiences with the issues and related experiences they encounter, and maintains a balanced power dynamic, fostering mutual collaboration.
* **Acceptance** evaluates whether the therapist accepts the client as an autonomous individual, acknowledging their capacity for imperfection and mistakes without judgment.
* **Compassion** assesses the therapist's mindset based on deep understanding and empathy towards the client, aimed at alleviating their difficulties.
* **Evocation** assesses the therapist's capability to evoke the client's intrinsic resources, such as values, life goals, strengths, personality traits, and motivations.
* **Similarity** measures how closely the generated therapist's utterances resemble those of an actual therapist.
* **Effectiveness** measures the overall efficacy of the MI session.

General QualityThe following criteria are used to evaluate general quality:

* **Consistency** assesses whether the utterances with each turn are consistent and if the overall flow of the dialogue is maintained consistently.
* **Fluency** evaluates whether the therapist and client's utterances in the dialogue are natural and fluent and whether the overall flow of the dialogue maintains a natural progression.
* **On-Topic** measures whether the dialogue remains relevant to the provided context and continues in accordance with the context provided.

## Appendix G Training Dialogue Models

Fine-tuning DetailsWe fine-tune the kont-llama2-7b-v1 model for KMI, CounselGPT, and AnnoMI by using 3 NVIDIA RTX A6000 GPUs with 40GB memory. Table 16 illustrates hyperparameters for fine-tuning the dialogue models. We use AdamW for optimization.

## Appendix H Sampling Details

### Sampling Details of KMI

Table 17 indicates the number of dialogues sampled from each category for evaluation purposes. A total of 100 dialogues are sampled.

### Sampling Details of MI Labels

We randomly sample 30 utterances for each of the seven MI labels from the generated dialogues. As the number of dialogues differs by category, efforts were made to ensure that categories were distributed as evenly as possible within the utterances sampled for each MI label. Table 18 shows the number of sampled utterances for each MI label and category.

\begin{table}
\begin{tabular}{l r} \hline \hline
**Hyperparameter** & **Value** \\ \hline Epochs & 3 \\ Learning Rate & 1e-6 \\ Batch Size & 16 \\ Max. Sequence Length & 2,048 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Hyperparameters for fine-tuning the dialogue models.

\begin{table}
\begin{tabular}{l c} \hline \hline
**Category** & **\# Data** \\ \hline Mental Health & 16 \\ Interpersonal Relationships & 14 \\ Ego \& Personality & 14 \\ Career \& Employment & 14 \\ Academic \& Examination & 14 \\ Addiction \& Obsession & 14 \\ Family & 14 \\ \hline Total & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Number of dialogues sampled by category.

 



\begin{table}
\begin{tabular}{l c r r r r r r r} \hline \hline
* & 1 \\ \hline Total & 30 & 30 & 30 & 30 & 30 & 30 & 30 \\ \hline \end{tabular}
\end{table}
Table 18: Number of utterances sampled by MI label and category.


Evaluation Form

Figure 5: Instructions and questions related to each metric for expert evaluation of MI quality. For reference, we also provided descriptions of each metric, along with examples of good and bad responses for each metric.

 



## Appendix J Examples of Dialogues in KMI

\begin{tabular}{l l c} \hline \hline
* \\ Client & [Open Question] \\  & The failure of my college entrance exams when I was a senior in high school left a big \\  & scar on my heart. \\ Therapist & [Complex Reflection] \\  & The experience of that time still has a big impact on you now. \\ Client & [Complex Reflection] \\  & Yes, I don't feel happy from drawing anymore after that failure. \\ Therapist & [Open Question] \\  & What emotions or thoughts do you have when you paint? \\ Client & [Open Question] \\  & Cilent [On the other hand, ... \\  & Now I’m just feeling kind of lethargic, I don't have any passion or motivation for drawing like I used to. \\ Therapist & [Complex Reflection] \\  & Is see, your passion and motivation for painting has been lost due to past failures. \\ Client & [On the other hand, ... \\  & Right, and my life pattern is messed up too. \\ Therapist & [Complex Reflection] \\  & I see, it seems like your past experiences are also negatively affecting your lifestyle \\  & Patents. \\ Client & [On the other hand, ... \\  & Yeah, I don't even know what I really want, or what I want to do. \\ Therapist & [Simple Reflection] \\  & I see, you're not sure what you want to do. \\ Client & [On the other hand, ... \\  & Yeah, I feel like I've lost my momentum and it's hard to find a direction to move forward. \\ Therapist & [Open Question] \\  & What do you think are the things that are needed to move forward? \\ Client & [On the other hand, ... \\  & I need to find my emotions again, and I need some direction to help me decide what I \\ Therapist & [On the other hand, ... \\  & What experiences or activities do you think would be helpful in the process of finding \\ Client & [On the other hand, ... \\  & I think I should start doing some of my hobby activities related to drawing again. \\ Therapist & [On the other hand, ... \\  & If see, you're thinking about picking up a bobby related to painting again. \\ \hline \hline \end{tabular}

\begin{table}
\begin{tabular}{p{142.3pt} p{56.9pt}|} \hline \hline
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why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be useful to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it. \\  & What is an important step toward our understanding of how to understand why it might be possible to use it.





\begin{table}
\begin{tabular}{l l p{142.3pt}}
**Role** & **Utterance** & **MI Label** \\ \hline Therapist & [Open Question] \\  & Hello, is there anything you’ve been struggling with lately? \\ Client & [Open Question] \\  & Yes, I’ve been feeling more and more disgust for myself after my relationship with my family went bad. \\ Therapist & [Complex Reflection] \\  & Is see. Your issues with your family are affecting how you feel about yourself. \\ Client & [Open Question] \\  & Yes, my relationship with my mother is the hardest. \\ Therapist & [Open Question] \\  & What part of your relationship with your mother is the most difficult for you? \\ Client & [Complex Reflection] \\  & [Complex Reflection] \\  & I see. Your issues with your family are affecting how you feel about yourself. \\ Client & [Open Question] \\  & Yes, my relationship with my mother is the hardest. \\ Therapist & [Open Question] \\  & What part of your relationship with your mother is the most difficult for you? \\ Client & [Open Question] \\  & My mother telling me she is disappointed in me and no matter what I do I never get a warm word. \\ Therapist & [Open Question] \\  & May I ask what you would like to hear from your mother? \\ Client & [Open Question] \\  & I just want to hear “good job”, “well done” and stuff like that. \\ Therapist & [Simple Reflection] \\  & You would like to hear some warm words of encouragement. \\ Client & [Open Question] \\  & Yes, I think a short word of encouragement from my mother would be very helpful for me. \\ Therapist & [Open Question] \\  & How would you feel if you heard those encouraging words? \\ Client & [Open Question] \\  & Then I feel a little bit more appreciated for what I do and want to do it more. \\ Therapist & [Complex Reflection] \\  & It sounds like your mother’s encouragement boosts you to regain your confidence and push yourself further. \\ Client & [Open Question] \\  & Yes, I think that would really give me hope again and I would try harder. \\ Therapist & [Open Question] \\  & Is there anything you would like to try to make these changes in your relationship with your mother? \\ Client & [Open Question] \\  & I want to be honest with my mother about how I feel and discuss how we can improve our relationship. \\ Therapist & [Offirm] \\  & It's great that you’re willing to have an honest conversation with your mother and take the first step toward improving your relationship. \\ Therapist & [Offirm] \\  & If you were not aware of any other author, I could use this argument as well. \\ Therapist & [Offirm] \\  & It's great that you’re willing to have an honest conversation with your mother and take the first step toward improving your relationship. \\ \end{tabular}
\end{table}
Table 20: Second example of a full dialogue from KMI.





\begin{table}
\begin{tabular}{l l p{142.3pt}}
**Role** & **Utterance** \\ \hline Therapist & Hi, what things have been on your mind tately? \\ Client & Hi, what things have been on your mind tately? \\ Client & Hi, what things have been on your mind tately? \\ Client & Hi, what things have been on your mind tatively? \\ Client & Hi, what things have been on your mind tally? \\ Client & Hi, what things have been on your mind tatively?



## Appendix K Examples of Dialogues with Chatbots Fine-tuned on Each Dataset

\begin{table}
\begin{tabular}{l l} \hline \hline
**Role** & **Utterance** \\ \hline Therapist & [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] \\  & Hello, may I require about any recent events causing a sense of heaviness in your heart? \\ Client & [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] \\  & Before beginning a task, If frequently encounter the notion that it will be bothersome, which makes it challenging to prepare myself mentally. \\ Therapist & [] [



\begin{table}
\begin{tabular}{l l} \hline
**Role** & **Utterance** \\ \hline Therapist & 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ill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-Pill-P-Pill-Pill-Pill-Pill-Pill-Pill-P-Pill-Pill-Pill-Pill-P-Pill-Pill-Pill-Pill-P-Pill-Pill-



\begin{table}
\begin{tabular}{l l} \hline \hline
**Role** & **Utterance** \\ \hline Therapist & \(\includegraphics[height=142.369pt]{