# Incorporating Different Verbal Cues to Improve Text-Based Computer-Delivered Health Messaging arXiv: https://arxiv.org/abs/2404.13633 ยท HTML: https://mlsanity.com/paper/2404.13633/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Builds on 29 of its 353 references that are in the map. - First posted on arXiv 21 Apr 2024. ## Builds on (most cited first, 29 of 29) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018) - [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019) - [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022) - [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022) - [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018) - [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023) - [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023) - [Beyond Goldfish Memory: Long-Term Open-Domain Conversation](https://mlsanity.com/paper/2107.07567/) (2021) - [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://mlsanity.com/paper/2004.08449/) (2020) - [Long Time No See! Open-Domain Conversation with Long-Term Persona Memory](https://mlsanity.com/paper/2203.05797/) (2022) - [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013) - [ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports](https://mlsanity.com/paper/2212.14882/) (2022) - [Building a Conversational Agent Overnight with Dialogue Self-Play](https://mlsanity.com/paper/1801.04871/) (2018) - [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017) - [Keep Me Updated! Memory Management in Long-term Conversations](https://mlsanity.com/paper/2210.08750/) (2022) - [Putting GPT-3's Creativity to the (Alternative Uses) Test](https://mlsanity.com/paper/2206.08932/) (2022) - [ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness](https://mlsanity.com/paper/2305.12947/) (2023) - [ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models](https://mlsanity.com/paper/2306.04563/) (2023) - [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://mlsanity.com/paper/1811.00207/) (2018) - [CAiRE: An Empathetic Neural Chatbot](https://mlsanity.com/paper/1907.12108/) (2019) - [Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages](https://mlsanity.com/paper/2308.13479/) (2023) - [Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions](https://mlsanity.com/paper/2308.04879/) (2023) - [Different but Equal: Comparing User Collaboration with Digital Personal Assistants vs. Teams of Expert Agents](https://mlsanity.com/paper/1808.08157/) (2018) - [Multi-Level Micro-Randomized Trial: Detecting the Proximal Effect of Messages on Physical Activity](https://mlsanity.com/paper/2007.13741/) (2020) ## Abstract (arXiv) The ubiquity of smartphones has led to an increase in on demand healthcare being supplied. For example, people can share their illness-related experiences with others similar to themselves, and healthcare experts can offer advice for better treatment and care for remediable, terminal and mental illnesses. As well as this human-to-human communication, there has been an increased use of human-to-computer digital health messaging, such as chatbots. These can prove advantageous as they offer synchronous and anonymous feedback without the need for a human conversational partner. However, there are many subtleties involved in human conversation that a computer agent may not properly exhibit. For example, there are various conversational styles, etiquettes, politeness strategies or empathic responses that need to be chosen appropriately for the conversation. Encouragingly, computers are social actors (CASA) posits that people apply the same social norms to computers as they would do to people. On from this, previous studies have focused on applying conversational strategies to computer agents to make them embody more favourable human characteristics. However, if a computer agent fails in this regard it can lead to negative reactions from users. Therefore, in this dissertation we describe a series of studies we carried out to lead to more effective human-to-computer digital health messaging. In our first study, we use the crowd [...] Our second study investigates the effect of a health chatbot's conversational style [...] In our final study, we investigate the format used by a chatbot when [...] In summary, we have researched how to create more effective digital health interventions starting from generating health messages, to choosing an appropriate formality of messaging, and finally to formatting messages which reference a user's previous utterances.