Idée - Human-Computer Interaction - # Generating Diverse and Efficacious Health Messages

Improving Text-Based Computer-Delivered Health Messaging Through Diverse and Appropriate Verbal Cues

Q: How can the insights from this work be applied to generate diverse and efficacious health messages using large language models (LLMs) instead of crowd workers?

The insights from the research on generating diverse and efficacious health messages can be applied to using large language models (LLMs) by leveraging the capabilities of these models to generate a wide range of message variations. LLMs, such as GPT-3 or GPT-4, have shown proficiency in natural language generation and can be trained on diverse datasets to understand context and generate relevant content. To apply these insights to LLMs, researchers can pre-train the models on a corpus of health-related data to ensure they have the necessary knowledge base. Additionally, techniques like prompt engineering can be used to guide the LLMs towards generating diverse messages by providing varied prompts that encourage creative ideation. This can help in avoiding redundancy and ensuring a broad spectrum of messaging styles and content. Furthermore, the evaluation framework developed in the research can be adapted to assess the diversity and efficacy of messages generated by LLMs. By measuring factors like creativity, informativeness, and engagement, researchers can gauge the quality of the messages produced by LLMs and make necessary adjustments to enhance their effectiveness in delivering health-related information.

Q: What other conversational cues beyond style (e.g., empathy, personality) could be explored to further improve the effectiveness of computer-delivered health messaging?

In addition to conversational style, exploring other conversational cues like empathy, personality, and tone can significantly enhance the effectiveness of computer-delivered health messaging. Empathy: Incorporating empathetic language and responses in health messaging can create a sense of understanding and support for the users. Empathetic cues can include acknowledging the user's feelings, providing emotional support, and showing compassion in responses. Personality: Tailoring the chatbot's personality to align with the target audience can improve engagement and trust. A friendly, professional, or authoritative personality can influence how users perceive and interact with the chatbot. Tone: The tone of the messages can impact the user's receptiveness and engagement. A positive and encouraging tone can motivate users, while a neutral or informative tone can convey professionalism and credibility. Language Complexity: Adapting the language complexity to match the user's understanding level can improve communication. Using simple language for complex medical terms and providing explanations can enhance user comprehension. By incorporating these additional conversational cues, computer-delivered health messaging can become more personalized, engaging, and supportive, leading to better user experiences and outcomes.

Q: How might the findings on referencing previous user utterances apply to other domains beyond health, such as personal assistants or educational chatbots?

The findings on referencing previous user utterances can be applied to other domains beyond health, such as personal assistants or educational chatbots, to enhance user interactions and experiences. Personal Assistants: In personal assistant applications, referencing previous user utterances can improve continuity and context in conversations. By recalling past interactions, personal assistants can provide more personalized and efficient assistance to users, leading to a smoother user experience. Educational Chatbots: Educational chatbots can benefit from referencing previous user utterances to track learning progress, provide tailored feedback, and adapt the educational content based on the user's past interactions. This can enhance the effectiveness of educational chatbots in delivering personalized learning experiences. Customer Service: In customer service applications, referencing previous interactions can help in resolving issues more effectively, understanding customer preferences, and building stronger relationships with users. By implementing a system that intelligently references and utilizes past user utterances, applications in various domains can improve user engagement, satisfaction, and overall performance. This approach can create a more personalized and seamless user experience across different types of conversational interfaces.

Concepts de base

Generating diverse and efficacious text-based health messages can be achieved by using a system that automatically selects collectively diverse example prompts to guide crowd-sourced message writing. Additionally, adopting an appropriate formal conversational style and referencing a user's previous utterances in a paraphrased manner can improve the effectiveness of computer-delivered health messaging.

Résumé

This dissertation investigates how to make text-based computer-delivered health messaging more effective through the use of verbal cues.

The first study focuses on generating diverse and efficacious health messages. It develops a system called "Directed Diversity" that uses word embeddings to automatically select diverse example prompts to show to crowd workers, who then write messages based on those prompts. Through a series of user studies, the researchers found that Directed Diversity led to more diverse and informative messages compared to baseline conditions.

The second study examines the effect of a health chatbot's conversational style on user perceptions and the quality of information shared. Two user studies on Amazon Mechanical Turk found that users perceived a chatbot using a formal conversational style as more competent and appropriate, especially when discussing sensitive medical information. Users also provided more specific and actionable responses when conversing with a formally-styled chatbot.

The final study investigates how a chatbot should reference a user's previous utterances. A three-week longitudinal study found that while users found chatbots that referenced previous utterances (either verbatim or paraphrased) more intelligent and engaging, the verbatim referencing also raised privacy concerns. Interviews provided further insights on user preferences and concerns.

Overall, this work demonstrates that generating diverse health messages, adopting an appropriate formal conversational style, and referencing previous user utterances in a paraphrased manner can improve the effectiveness of text-based computer-delivered health messaging.

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arxiv.org

Stats

People can share illness-related experiences and get advice from healthcare experts through on-demand digital health messaging.
Computers as Social Actors (CASA) posits that people apply the same social norms to computers as they do to people.
Previous studies have focused on applying conversational strategies to computer agents to make them embody more favorable human characteristics.

Citations

"Encouragingly, computers are social actors (CASA) posits that people apply the same social norms to computers as they would do to people."
"Therefore, in this dissertation we describe a series of studies we carried out to lead to more effective human-to-computer digital health messaging."

Idées clés tirées de

Incorporating Different Verbal Cues to Improve Text-Based Computer-Delivered Health Messaging

by Samuel Rhys ... à arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13633.pdf

Incorporating Different Verbal Cues to Improve Text-Based Computer-Delivered Health Messaging

Questions plus approfondies

How can the insights from this work be applied to generate diverse and efficacious health messages using large language models (LLMs) instead of crowd workers?

The insights from the research on generating diverse and efficacious health messages can be applied to using large language models (LLMs) by leveraging the capabilities of these models to generate a wide range of message variations. LLMs, such as GPT-3 or GPT-4, have shown proficiency in natural language generation and can be trained on diverse datasets to understand context and generate relevant content.
To apply these insights to LLMs, researchers can pre-train the models on a corpus of health-related data to ensure they have the necessary knowledge base. Additionally, techniques like prompt engineering can be used to guide the LLMs towards generating diverse messages by providing varied prompts that encourage creative ideation. This can help in avoiding redundancy and ensuring a broad spectrum of messaging styles and content.
Furthermore, the evaluation framework developed in the research can be adapted to assess the diversity and efficacy of messages generated by LLMs. By measuring factors like creativity, informativeness, and engagement, researchers can gauge the quality of the messages produced by LLMs and make necessary adjustments to enhance their effectiveness in delivering health-related information.

What other conversational cues beyond style (e.g., empathy, personality) could be explored to further improve the effectiveness of computer-delivered health messaging?

In addition to conversational style, exploring other conversational cues like empathy, personality, and tone can significantly enhance the effectiveness of computer-delivered health messaging.

Empathy: Incorporating empathetic language and responses in health messaging can create a sense of understanding and support for the users. Empathetic cues can include acknowledging the user's feelings, providing emotional support, and showing compassion in responses.

Personality: Tailoring the chatbot's personality to align with the target audience can improve engagement and trust. A friendly, professional, or authoritative personality can influence how users perceive and interact with the chatbot.

Tone: The tone of the messages can impact the user's receptiveness and engagement. A positive and encouraging tone can motivate users, while a neutral or informative tone can convey professionalism and credibility.

Language Complexity: Adapting the language complexity to match the user's understanding level can improve communication. Using simple language for complex medical terms and providing explanations can enhance user comprehension.

By incorporating these additional conversational cues, computer-delivered health messaging can become more personalized, engaging, and supportive, leading to better user experiences and outcomes.

How might the findings on referencing previous user utterances apply to other domains beyond health, such as personal assistants or educational chatbots?

The findings on referencing previous user utterances can be applied to other domains beyond health, such as personal assistants or educational chatbots, to enhance user interactions and experiences.

Personal Assistants: In personal assistant applications, referencing previous user utterances can improve continuity and context in conversations. By recalling past interactions, personal assistants can provide more personalized and efficient assistance to users, leading to a smoother user experience.

Educational Chatbots: Educational chatbots can benefit from referencing previous user utterances to track learning progress, provide tailored feedback, and adapt the educational content based on the user's past interactions. This can enhance the effectiveness of educational chatbots in delivering personalized learning experiences.

Customer Service: In customer service applications, referencing previous interactions can help in resolving issues more effectively, understanding customer preferences, and building stronger relationships with users.

By implementing a system that intelligently references and utilizes past user utterances, applications in various domains can improve user engagement, satisfaction, and overall performance. This approach can create a more personalized and seamless user experience across different types of conversational interfaces.