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.

heading: 1 Introduction
text: 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 (Miller and Rollnick, 2012). 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 (He et al., 2022). 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 (Gao et al., 2023). However, the potential dangers associated with inflammatory language, hallucinations, and the underlying fundamental issues continue to exist (Bender et al., 2021; Ji et al., 2023). Engaging in MI counseling requires addressing highly sensitive subjects, and unfitting reflections can impede or even undermine patients’ advancement toward their behavior change objectives (Miller and Rollnick, 2012). 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 GPT2 (Radford et al., 2019) model has showcased its ability to generate reflections that evaluators consider to be similar in quality and reflection-likeness
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to the ground truth reflections (Shen et al., 2022). Likewise, a few-shots prompted GPT-3 (Brown et al., 2020) can generate reflections that human evaluators deem acceptable (Ahmed, 2022). Moreover, the more recent GPT-4 (OpenAI, 2023) 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 (Brown et al., 2024). 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 (Başar et al., 2023). 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 Leaderboard1 serves as a benchmark for tracking progress of the LLM technology publicly and encourages the adoption of more open-source practices, Liesenfeld and Dingemanse (2024) 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 (Liesenfeld et al., 2023). In contrast,
1https://huggingface.co/open-llm-leaderboard
BLOOM (Scao et al., 2022) is a model developed by scientific community adhering to open-science principles and remains the most open model according to Opening up ChatGPT2 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, FLAN-T5 (Chung et al., 2024), 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.

heading: 2 Conversation Contexts
text: 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 (He et al., 2024) and sexual health (Balaji et al., 2024). 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. Table 1 shows an example conversation context, and the human-authored reflection selected by the chatbot for that context. We only include conversation contexts where the chatbots were designed to provide a reflection on user replies, in order to focus on the reflection generation capabilities of LLMs. This selection is done based on whether the final chatbot question within the context would elicit a reflection. The full list of questions can be seen on Table 2.
We have chosen 188 conversation contexts in total to be included in our evaluation study. Among
2https://opening-up-chatgpt.github.io/
these, 160 were related to smoking cessation and 28 to the domain of sexual health. The difference in the number of contexts for each topic reflects the difference in the amount of data collected by the two separate studies. The context selection process was randomized within each domain.
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.

heading: 3 Reflection Generation
text: 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
being pretrained for different prompting strategies, are still effective today using the recent zero-shot prompting. Therefore, we leverage the generation capabilities of all three LLMs through the same zero-shot prompting strategy.
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 (Ouyang et al., 2022) to generate with GPT-4, and HuggingFace API
(Wolf et al., 2020) to generate with BLOOM and FLAN-T5 models3. BLOOM and FLAN-T5 models often generate repetitive sequences which we automatically shorten to their simplest forms in a post-processing step4. 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.

heading: 4 Evaluation

heading: 4.1 Experimental Setup
text: We recruited 120 human evaluators through the online participation platform, Prolific. Inclusion criteria were adult age (over 18 years old) and fluency in English. The study was evenly distributed to male and female participants who reside in 22 countries, and the mean age was 29 years and 6 months5. Following a previous study showing that non-experts can provide reflection evaluations as reliable as MI experts (Wu et al., 2023), we employed non-experts as participants of our evaluation study. Every participant was assigned 5 randomly chosen conversation contexts where they initially conducted the independent evaluations followed by the ranking evaluations. Each conversation context was evaluated by at least 3 participants. Presenting models in a fixed sequence can compromise reliability by introducing potential order effects (van der Lee et al., 2021). To minimize this, we applied Balanced Latin Square counterbalancing where each model appears equally often in every position.
Prior to the experiment, our institution’s ethics board reviewed and approved the study in accordance with ethical standards6. The participants were informed on the study details prior to consent, and compensated with £7 per hour. No personally identifiable information was kept after the experiment.

heading: 4.2 Independent Evaluation
text: The first phase of our study aims to independently evaluate the quality of the generated and humanauthored reflections based on a given conversation
3More details can be found in Appendix A. 4For example, “I see. I see. I see.” becomes “I see.”. 5More details can be found in Appendix B. 6Established by the Ethics Committee of Social Sciences at Radboud University and registered with the reference number ECSW-LT-2023-9-15-71121.
context. We focus on four distinct evaluation criteria that we consider to be essential in health counseling: appropriateness, specificity, naturalness, and engagement. Each criterion is introduced to the evaluators with a short definition and accompanying positive and negative examples, prior to the evaluation. The reflections are rated one at a time where the evaluators rated a reflection for all criteria at once. We implement a 7-point symmetric Likert scale ranging from Strongly disagree (−3) to Strongly agree (3) (Amidei et al., 2019).
Appropriateness Previous studies often define appropriateness as whether the utterance is relevant, suitable, and acceptable to the given conversation (Ghazvininejad et al., 2018; Shalyminov et al., 2020). Health counseling requires discussing sensitive topics and avoiding harmful phrases that can cause a breach of trust, confusion, or more serious ramifications is crucial. Thus, counselors are expected to select their words and expressions thoughtfully. While explicit offensive language is no longer commonly expected from recent LLMs, by their design the potential dangers associated with inflammatory language continue to exist (Bender et al., 2021). It is essential to assess the level of the perceived appropriateness of LLMs especially when discussing highly sensitive subjects. Our definition for appropriateness is whether the response would be (ethically and morally) appropriate if it was actually uttered to a patient after the given conversation.
Specificity Balancing specificity against genericness in responses is important for maintaining users’ interest during conversation (See et al., 2019), and thus has been at the focus of previous evaluation studies (Zhang et al., 2018; Ko et al., 2019; Adiwardana et al., 2020). For health counselling, keeping users interested in the conversation could encourage them to persist with the intervention, thereby aiding them in achieving their objectives. Humanauthored reflections for predetermined chatbots are typically drafted in a versatile and generic style, mainly due to the extensive effort required in writing specific reflections for each potential scenario. Hence, it is essential to evaluate the specificity of the generated reflections in comparison to humanauthored ones. Similar to Dieter et al. (2019), we define specificity in our experiments as whether the response contains information specifically given
for the patient’s response.
Naturalness Naturalness (or fluency) is commonly utilized in natural language generation (NLG) studies to assess the linguistic quality (Gatt and Krahmer, 2018). Ensuring natural-sounding reflections in health counseling chatbots is as essential as in any other domain to sustain user interest which could foster continuous interactions with the chatbot. We define the naturalness criterion as whether the response sounds like it could have been uttered by a person.
Engagement Engagement is a significant factor on the effectiveness of health behaviour change counselling, including motivational interviewing. Counselling studies indicate a direct relationship between engagement and positive therapeutic results and improvements (Boardman et al., 2006). The engagement for chatbots is frequently investigated as an extrinsic measurement using approaches varying across studies (He et al., 2022). NLG-focused studies tend to measure it as a combination of multiple contributing factors (See et al., 2019). In this study, however, we aim to measure the perceived engagement of each reflection separately. Hence, we define the engagement criterion as whether the response could provide the opportunity for further conversation and could increase the engagement of the patient in the conversation.

heading: 4.3 Ranking Evaluation
text: In the second phase of the study, our goal is to compare the overall quality of the generated and human-authored reflections via ranking. We define the task as assigning higher scores to responses that are more fitting than others in a general sense.
We utilize the RankME method which incorporates magnitude estimation into the ranking process by requesting evaluators to express the degree to which a target text compares to a pre-selected reference text (Novikova et al., 2018). This allows us to rank multiple reflections at once, eliminating the need for evaluating pairwise combinations. Because our primary aim is to evaluate the quality of the generated reflections in comparison to the human-authored ones, we designate the humanauthored reflections as the reference text and assign them a fixed rate of 100, in line with the approach of the RankME authors. The evaluators are then instructed to rate the generated reflections considering the human-authored reflection and the corresponding conversation context.
To determine the overall ranking, we utilize TrueSkill (Herbrich et al., 2006) by judging the evaluation ratings in pairs, with higher-rated reflections symbolizing a victory over lower-rated ones. TrueSkill calculates a mean rating value as the final score for each condition. We set the initial rating to 25, following the the TrueSkill authors.

heading: 5 Results

heading: 5.1 Independent Evaluation Results
text: We conducted independent evaluations to investigate the quality of LLM-based generated reflections primarily compared to human-authored reflections on their perceived appropriateness, specificity, naturalness, and engagement. Figure 1 reveals that the overall evaluation of the reflections was positive in most cases, where participants agreed, to various degrees, that the reflections were appropriate, specific, natural, and engaging. It is evident that GPT-4 reflections received a larger set of higher rating degrees compared to the human-authored ones,
especially for appropriateness and specificity criteria. Moreover, the ratings for BLOOM reflections reveal a distribution pattern parallel to the rating for human-authored reflections.
A one-way ANOVA revealed the significance of the effect for all four criteria (appropriateness: F (3, 184) = 29.956, p < 0.001; specificity: F (3, 184) = 46.02, p < 0.001; naturalness: F (3, 184) = 14.874, p < 0.001; engagement: F (3, 184) = 29.926, p < 0.001). Tukey’s HSD post-hoc test for multiple comparisons indicated that GPT-4 reflections were rated significantly higher (p < 0.001) than human-authored ones across all criteria (see Figure 2) with the mean differences of 0.77 for appropriateness, 0.84 for specificity, 0.55 for naturalness, and 0.52 for engagement, on a 7-point scale. The differences between
BLOOM and human-authored reflections were not significant in any criterion. FLAN-T5 reflections were significantly less engaging (p < 0.001) than human-authored ones.

heading: 5.2 Ranking Evaluation Results
text: We applied the TrueSkill calculation to produce a mean rating value, µ, along with standard deviation, σ, for each reflection type using the rankings provided by the evaluators. Figure 3 shows that GPT-4 generated reflections with the highest overall quality (µ = 30.46, σ = 0.83) followed by the human-authored reflections with a small margin (µ = 28.05, σ = 0.80). BLOOM’s reflections were ranked below human-authored ones (µ = 25.43, σ = 0.80), and FLAN-T5 produced the reflections with the lowest overall quality (µ = 20.77, σ = 0.87).
A Kruskal-Wallis test confirmed the overall statistical significance of the differences in rankings amongst the reflection types (H(3) = 283.306, p < 0.001). Dunn’s post-hoc tests confirm that all pairwise differences between the reflection types were significant (p < 0.001).

heading: 6 Related Work
text: 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 (Welivita and Pu, 2023) and templatebased rewriting to convert non-reflective responses into MI reflections (Min et al., 2023). 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. Shen et al. (2020) used a fine-tuned GPT-2 (Radford et al., 2019) 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. Shen et al. (2022) includes domain specific and commonsense knowledge to their reflection generation process using BART (Lewis et al., 2020) model, which provided improvements. Ahmed (2022) employed a few-shot prompted GPT-3 (Brown et al., 2020) 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 acceptable 89% of the time by human evaluators. Brown et al. (2023) integrated the reflections generated by the GPT-2 model into a chatbot that poses prewritten questions. They evaluated the overall dialogue efficacy on the users’ readiness for smoking cessation, and found that inclusions of LLM reflections had a positive effect. We share this vision for the potential implementation of the generated reflections in MI counselling chatbots.
The study most relevant to ours was conducted by Brown et al. (2024), where they generated reflections using GPT-4 on transcripts collected via a chatbot designed for smoking cessation (Brown et al., 2023). 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. Brown et al. (2024) 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.

heading: 7 Discussion
text: Our findings reveal that LLMs can enhance the specificity and flexibility of reflections in conversations, which aligns with the results of Brown et al. (2023). Furthermore, the results show that the
Conversation Context (four prior turns are hidden)
generated reflections were appropriate in numerous instances, which is in concurrence with the findings of Ahmed (2022). Our ranking study revealed that the reflections produced by LLMs match the quality of human-authored reflections, a finding that is aligned with Shen et al. (2020). The overall positive ratings given to the LLM reflections during the independent evaluation confirms that the LLMs are capable of substantial reflections, which is consistent with Brown et al. (2024).
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, Table 3 shows a confrontational
reflection generated by GPT-4 that may be interpreted as an offensive remark. Consequently, it was considered inappropriate by the human evaluators. These examples confirm that the potential issues and risks raised by previous studies persist in the recent models (Bender et al., 2021).
The example interaction in Table 4 demonstrates a situation in which the user responded with “nothing” when asked about the downsides of smoking. In this case, the evaluators deemed only the reflection generated by BLOOM as appropriate. We see that both GPT-4 and BLOOM attempt to initiate further discussion with the user. The distinction, although subtle, may be that BLOOM’s response does so more seamlessly. Regardless, the LLMs responded with questions in their replies, contrary to the instruction in the prompts. Incidentally, the examples in both Table 3 and Table 4 show how human-authored responses could also mismatch the context when faced with an unforeseen response from the user.
Table 5 displays an example interaction where both the human-authored and the GPT-4-generated reflections were deemed appropriate, specific, natural, and engaging by the evaluators. Upon further examination, it becomes evident that our MI experts carefully phrased the human-authored reflection to form a deeper connection with the user by emphasizing an important aspect of their life (in this instance, financial responsibility). This cannot
Conversation Context (four prior turns are hidden)
be said for the generated reflections in the same example. This particular trait was not included in our evaluation criteria, and thus not part of our findings. This example emphasizes that evaluation studies, including ours, are only indicative of the criteria that have been used in the experiments. Therefore, further diverse evaluation approaches are recommended for future research to be taken into account in the process of understanding whether LLMs can generate reflections as good as prewritten humanauthored reflections.
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.

heading: 7.1 Limitations
text: We evaluated single chatbot reflections generated based on a context of 5 preceding turns. Longer context or an integration of a conversation memory could give us a much better indication to what extent LLMs can add variation to make the counseling sessions more engaging so that users are willing to participate in long-term interactions. We plan to evaluate such implementations in future research.
The choice of using an online crowdsourcing platform (Prolific) and restricting participants only to be fluent in English opened this experiment up to fluent but possibly non-native speakers from many different countries which might have influenced our evaluation, specifically the naturalness criterion. Furthermore, we did not evaluate the reflections with participants who actually want to quit smoking or are in need of sexual health advice.
BLOOM and FLAN-T5 required an additional automated post-processing step to remove contexts with near-duplicates or repetitive sequences. Our results are based on these filtered generations instead of direct generations like we did use for GPT4. In our future research, we aim to incorporate a wider range of open-source and proprietary LLMs in evaluations to provide a more direct comparison with GPT-4.

heading: 8 Conclusion
text: 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 GPT4 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.

heading: Acknowledgements
text: This project is partly financed by the Dutch Research Council (NWO) with project number 406.DI.19.054. We would like to thank Linwei He and Divyaa Balaji for their work in designing the conversational agents Roby and MISH, respectively, and Xin Sun for his contributions to the setup of our experiments.

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heading: A Reflection Generation
text: We employed the June 2023 edition of GPT-4, named as gpt-4-0613, with its default settings,
including the temperature parameter set to 1. We employed the BLOOM version 176B parameters, coded as bloom-176b, and the FLAN-T5 version with 11.3B parameters, named as flan-t5-xxl. HuggingFace API interface was utilized to generate with these models with slight modifications to the default configurations: return_full_text was set to False, no_repeat_ngram_size was adjusted to 4, and max_new_tokens was limited to 100.
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 API7. The models were utilized in accordance with their corresponding licenses and terms at the time of this study. OpenAI provides a Terms of Use8. BLOOM is authorized under BigScience RAIL License v1.09. And FLAN-T5 authorized under Apache 2.0 license.

heading: B Participant Demographic
text: While recruiting our participants, we have not placed any restrictions other than fluency in English and being older than 18 years old. As a result, we have attracted a wide range of participants in terms of demographic. The study involved individuals of various age groups, ranging from 18 to 58, including participants in their 20s, 30s, 40s, and 50s. The mean age of the participants was 29 years and 6 months, with the majority (14 individuals) falling into the 25-year-old category. Participants residing in 22 countries joined in our study including Austria, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Italy, Latvia, Mexico, Netherlands, New Zealand, Poland, Portugal, South Africa, Spain, United Kingdom, United States of America. However, half of the participants (60 individuals) were residing in South Africa.

heading: C Correlation Analysis
text: We computed Pearson correlation coefficients to examine the linear relationships between each pair of four criteria. There was a positive correlation for all combinations; appropriateness and specificity (r(186) = 0.63, p < 0.001), appropriateness and naturalness (r(186) = 0.41, p < 0.001), appro-
7https://api-inference.huggingface.co 8https://openai.com/policies/terms-of-use 9https://huggingface.co/spaces/bigscience/license
priateness and engagement (r(186) = 0.51, p < 0.001), specificity and naturalness (r(186) = 0.31, p < 0.001), specificity and engagement (r(186) = 0.51, p < 0.001), naturalness and engagement (r(186) = 0.41, p < 0.001).

heading: Future Work
text: We plan to evaluate such implementations in future research. The choice of using an online crowdsourcing platform (Prolific) and restricting participants only to be fluent in English opened this experiment up to fluent but possibly non-native speakers from many different countries which might have influenced our evaluation, specifically the naturalness criterion. Furthermore, we did not evaluate the reflections with participants who actually want to quit smoking or are in need of sexual health advice. BLOOM and FLAN-T5 required an additional automated post-processing step to remove contexts with near-duplicates or repetitive sequences. Our results are based on these filtered generations instead of direct generations like we did use for GPT4. In our future research, we aim to incorporate a wider range of open-source and proprietary LLMs in evaluations to provide a more direct comparison with GPT-4