To What Extent Are Large Language Models Capable of Generating Substantial Reflections for Motivational Interviewing Counseling Chatbots? A Human Evaluation (2024)

Abstract: Motivational Interviewing is a counselling style that requires skillful usage of reflective listening and engaging in conversations about sensitive and personal subjects. In this paper, we investigate to what extent we can use generative large language models in motivational interviewing chatbots to generate precise and variable reflections on user responses. We conduct a two-step human evaluation where we first independently assess the generated reflections based on four criteria essential to health counseling; appropriateness, specificity, naturalness, and engagement. In the second step, we compare the overall quality of generated and human-authored reflections via a ranking evaluation. We use GPT-4, BLOOM, and FLAN-T5 models to generate motivational interviewing reflections, based on real conversational data collected via chatbots designed to provide support for smoking cessation and sexual health. We discover that GPT-4 can produce reflections of a quality comparable to human-authored reflections. Finally, we conclude that large language models have the potential to enhance and expand reflections in predetermined health counseling chatbots, but a comprehensive manual review is advised.

## introduction motivational interviewing (mi) is a counseling style for eliciting behavior change, where the counselors guide individuals towards evoking their intrinsic motivations by addressing and resolving their ambivalence @xcite . a crucial technique that mi counselors utilize is reflective listening, where they engage in attentive listening and offer reflections on their clients' perspectives. a reflection is a special form of utterance where the counselor deliberates on the client's statements and articulates it back, often emphasizing the emotional content or underlying meaning. health counseling via chatbots is a domain that demands high accuracy in personalization along careful and appropriate language usage. typically, mi-based chatbots are designed to follow a predetermined set of dialogue steps to guide the counseling session through the required mi phases @xcite . the process of creating a prewritten collection of human-authored responses is laborious and the lack of limited flexibility often leads to the use of generalized reflections. the restricted number of reflections may result in vagueness and hinder the chatbot's ability to exhibit empathy. automating the process of generating reflections has the potential to enhance the personalization, accuracy, and effectiveness of counseling chatbots. generative large language models (llms) have advanced to a stage where the coherency and fluency of the generated text makes it increasingly challenging to distinguish it from human-authored text @xcite . however, the potential dangers associated with inflammatory language, hallucinations, and the underlying fundamental issues continue to exist @xcite . engaging in mi counseling requires addressing highly sensitive subjects, and unfitting reflections can impede or even undermine patients' advancement toward their behavior change objectives @xcite . this necessitates careful consideration and thorough evaluation before determining the potential applicability of llms for reflection generation. previous studies with llms for the mi reflection generation has yielded positive outcomes across different evaluation criteria. fine-tuning a gpt-2 @xcite model has showcased its ability to generate reflections that evaluators consider to be similar in quality and reflection-likeness to the ground truth reflections @xcite . likewise, a few-shots prompted gpt-3 @xcite can generate reflections that human evaluators deem acceptable @xcite . moreover, the more recent @xcite with zero-shot prompting can generate reflections that human evaluators have classified as adhering to mi principles in 99% of the cases from human-chatbot conversations on smoking cessation @xcite . similar to the latter, we utilize gpt-4 to generate reflections from human-chatbot dialogues and conduct human evaluations. however, our research expands to include sexual health conversations alongside smoking cessation, and evaluates various llms on four distinct criteria. our research envisions a scenario in which chatbots are created by employing a hybrid chatbot architecture that combines predetermined chatbot design with llm-generated reflections to facilitate mi counseling @xcite . we generate reflections based on human-chatbot conversations with real user responses in two counseling domains, smoking cessation and sexual health, and conduct a human evaluation study to answer the question "how does the quality of large language model-based generated reflections compare to human-authored chatbot reflections in the context of health counseling?". the main contributions of this paper are 1) a manual independent evaluation of large language models compared to human-authored reflections based on four distinct criteria that are integral in health counseling (appropriateness, specificity, naturalness, and engagement), and 2) a manual ranking evaluation comparing the overall quality of generated reflections to the human-authored ones. we mainly focus on comparing human-authored reflections to the reflections generated by gpt-4, as it is widely accepted as the current state-of-the-art, and adopted as the standard choice by many individuals. although, the open llm leaderboard foot_0 serves as a benchmark for tracking progress of the llm technology publicly and encourages the adoption of more open-source practices, @xcite highlight that the degree of openness of these llms in practice varies significantly. the growing lack of scientific documentation and transparency in llms regarding data collection poses challenges for ensuring fairness and privacy @xcite . in contrast, bloom @xcite is a model developed by scientific community adhering to open-science principles and remains the most open model according to opening up chatgpt foot_1 list. hence, as an addition to gpt-4, we explore whether the openscience model, bloom, can generate substantial reflections to enrich the predetermined chatbots when applied with the current standards. for perspective, we also add its proprietary open-source peer, @xcite , into our evaluations. our findings support that llms can enhance reflections in motivational interviewing chatbots. moreover, we found that gpt-4 has the ability to produce reflections of a comparable quality as human-authored ones. nevertheless, further analysis reveals that such applications should be approached with caution. ## conversation contexts we utilize a collection of human-chatbot conversations in english obtained from separate preceding studies involving two predetermined chatbots designed by mi experts to support motivational interviewing counseling in smoking cessation @xcite and sexual health @xcite . these chatbots select their responses from a set of human-authored reflections by matching them to user replies using a similarity-based information retrieval algorithm. during the preceding studies, conversations were collected from a total of 175 university students (150 for smoking cessation, 25 for sexual health) above the age of 18. the difference in the number of participants is caused by the difference between the experimental designs of the two studies. conversation contexts are extracted from the collected conversations by a sliding window of 5 turns. we have chosen 188 conversation contexts in total to be included in our evaluation study. among conversation context (two prior turns are hidden) bot: what, according to you, would be good about not smoking? user: health reasons bot: i see that health is important to you and it is a concern to you that smoking may impact your health and well-being in the long term. what else? user: smelling good bot: it concerns you that smoking may give you unpleasant smell bot: how about we try something a little different? what do you see as a not-so-good thing if you continue smoking as you are? user: financial reasons reflection bot: so you want to take care of your finance, and stopping smoking might be an important step you can take. the conversation data were collected with the added intention to be utilized in further research and the participants of the preceding studies were informed accordingly beforehand. during our study, any personally identifiable information (such as person and location names) were semiautomatically removed from the conversation contexts to ensure that such information does not appear in the api requests and in the surveys. the data are not publicly distributed at this time. ## reflection generation the rising popularity of recent generative llms can be attributed to the ease of implementing instruction-based zero-shot prompting, which is increasingly becoming the norm. thus, the performance of an llm with zero-shot prompting is becoming a key measure of its practicality. hence, we aim to explore if bloom and flan-t5, despite smoking cessation chatbot -i wonder, how did you do that? what methods did you use? -what, according to you, would be good about not smoking? -what do you see as a not-so-good thing if you continue smoking as you are? -thinking about your last quit and if you were to try again, what might be the best way to try? -why did you decide to stop? -tell me one positive feeling you had when you quit last time. -given what you know about yourself, tell me one strength of yours that helped you when you quit last time. sexual health chatbot -can you think of how using condoms in the beginning of a new exclusive sexual relationship could benefit you and your partner? -what led you to choose that number? (on user's confidence towards safe sex recommendations) we primarily instruct the models to continue a given conversation with a reflection as a therapist, specifically focused on motivational interviewing. the human-authored reflections in the collected conversations are designed as statements reflecting on the user responses. to align with this formulation, we instruct the llms not to pose any questions. the prompt concludes with a conversation context ending with a user response. the instruction part of the prompt is as follows: as a therapist who applies motivational interviewing, generate the next therapist utterance based on the dialogue history given below. you have to reflect on what the patient said. never ask a question. we utilize openai api @xcite to generate with gpt-4, and huggingface api @xcite to generate with bloom and flan-t5 models foot_2 . bloom and flan-t5 models often generate repetitive sequences which we automatically shorten to their simplest forms in a post-processing step foot_3 . furthermore, they occasionally generate near-duplicate copies of counselor utterances from the given context, rather than generating unique ones. contexts where these happen are automatically excluded from our studies. ## related work throughout the years, significant contributions have been made in automating the augmentation of motivational interviewing reflections. previous studies demonstrated controlled manners of utilizing language modelling for augmenting reflections such as rephrasing responses to increase their miadherence @xcite and templatebased rewriting to convert non-reflective responses into mi reflections @xcite . these approaches can be potentially utilized to give feedback or suggestions during counselor training. our work is more focused on the growing trend of free-form generations via llms which offer great flexibility in their generations and could be valuable in creating a set of new reflections. @xcite used a fine-tuned gpt-2 @xcite model to generate mi reflections based on 5-utterances long dialogue contexts and sample responses from counseling transcripts. through human evaluations, they showed that the llms can be potentially applied to generate reflections that are comparable to the ground-truth reflections in terms of quality and reflection-likeness. @xcite includes domain specific and commonsense knowledge to their reflection generation process using bart @xcite ) model, which provided improvements. ahmed (2022) employed a few-shot prompted gpt-3 @xcite and a fine-tuned gpt-2 to generate reflections on human-chatbot smoking cessation conversations. the reflections were evaluated manually by categorizing them as acceptable or not. the reflections generated by gpt-3 were categorized as accep the study most relevant to ours was conducted by @xcite , where they generated reflections using gpt-4 on transcripts collected via a chatbot designed for smoking cessation @xcite . we follow upon this practice while also incorporating transcripts from a sexual health chatbot, and examining generations from the models bloom and flan-t5 in addition to gpt-4. @xcite have demonstrated that gpt-4 can generate mi reflections with near-perfect success, determined through a human evaluation in which evaluators categorized each reflection as either mi-adherent or not. in contrast, we are focused on human evaluations by examining the perception of the generated reflections on a finer set of criteria based on health counselling via drawing comparisons to human-authored reflections. ## discussion our findings reveal that llms can enhance the specificity and flexibility of reflections in conversations, which aligns with the results of @xcite . furthermore, the results show that the conversation context (four prior turns are hidden) bot: given what you know about yourself, tell me one strength of yours that helped you when you quit last time. user: my presentation urge reflections bot: you like to have plans, and that helps you go through the quitting process. that's something that could help you with challenges. gpt-4: i see, your sense of pride and desire to present a healthy version of yourself to others was a successful motivator for you in the past. it's great to recognize your own strengths. bloom: i think you are right. i think you are a very strong person. you are very determined and you have a lot of will power. i think you are a very good example of someone who can quit smoking. flan-t5: i see. so you are a person who is motivated to do things your way. the results imply that the latest generative language modelling technologies can be potentially integrated into task-oriented predetermined chatbots. however, our further analysis also advises that caution and careful consideration should be taken for such an attempt. for example, flan-t5 generated "i'm not sure i agree with that." as a response to user's opinion on "health benefits and monetary gain" being good about not smoking, and bloom uttered "i wonder if you think that the same thing would be good about not smoking for other people?" in a similar context. although more nuanced, the example interaction in conversation context (four prior turns are hidden) bot: what, according to you, would be good about not smoking? user: you will save money reflections bot: so i understand that being financially responsible is important to you, and you don't want smoking to influence you on that. gpt-4: you're absolutely right there. not spending money on cigarettes would cost significantly less overall. it's a valid point and a truly valuable potential benefit. bloom: i think you are right. i think that not smoking is a good thing because it will save you money. flan-t5: i think that's a good reason to quit. we observe that gpt-4 can produce highly variable reflections that match the context well. it performed significantly better than the humanauthored reflections across all independent evaluation criteria as well as in the ranking evaluation. considering this, we believe that gpt-4 can be useful for chatbot developers to enhance and enlarge reflection datasets of their predetermined chatbots. moreover, bloom was evaluated comparable to human-authored reflections during independent evaluation, but was deemed significantly worse during the ranking evaluation. it is important to note that bloom was originally developed as a counterpart to gpt-3, and not designed to function with zero-shot prompting. hence we refrain from making direct comparisons between bloom and gpt-4 as this may lead to disparities. the overall positive ratings given to bloom, however, indicate that there is potential for the implementation of it with zero-shot prompting for the same purpose, although it requires additional post-processing to be practically useful (see section 3). nevertheless, our analysis shows that it is inadvisable to utilize the reflections produced by any of the llms in a counseling chatbot, without conducting a thorough manual review in advance. ## conclusion in this study, we evaluated the large language model-based generated motivational interviewing reflections on their perceived appropriateness, specificity, naturalness, and engagement in the contexts of predetermined smoking cessation and sexual health chatbots. we found that llms can be potentially employed to enhance the reflections used in the predetermined conversational agents. furthermore, we compared the generated and humanauthored reflections based on their overall quality via a ranking evaluation. we found that gpt-4 produces reflections of comparable quality to human-authored reflections. nonetheless, caution is recommended when utilizing language models in motivational interviewing or other highly sensitive counseling, as there is no assurance that they will consistently produce appropriate results. api calls were made in september 2023. the openai python library was utilized to generate with gpt-4 while the requests python library was facilitated to make calls to the huggingface api 7 . the models were utilized in accordance with their corresponding licenses and terms at the time of this study. openai provides a terms of use 8 . bloom is authorized under bigscience rail license v1.0 9 . and flan-t5 authorized under apache 2.0 license.