Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulness

• URL: http://arxiv.org/abs/2407.13067v1
• Date: Wed, 3 Jul 2024 15:43:16 GMT
• Title: Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulness
• Authors: Harsh Kumar, Suhyeon Yoo, Angela Zavaleta Bernuy, Jiakai Shi, Huayin Luo, Joseph Williams, Anastasia Kuzminykh, Ashton Anderson, Rachel Kornfield,
• Abstract summary: Large Language Models show promise in providing human-like dialogues that could emulate social support. We conducted two randomized experiments to assess the impact of LLM agents on user engagement with mindfulness exercises.
• Score: 17.055863270116333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although engagement in self-directed wellness exercises typically declines over time, integrating social support such as coaching can sustain it. However, traditional forms of support are often inaccessible due to the high costs and complex coordination. Large Language Models (LLMs) show promise in providing human-like dialogues that could emulate social support. Yet, in-depth, in situ investigations of LLMs to support behavior change remain underexplored. We conducted two randomized experiments to assess the impact of LLM agents on user engagement with mindfulness exercises. First, a single-session study, involved 502 crowdworkers; second, a three-week study, included 54 participants. We explored two types of LLM agents: one providing information and another facilitating self-reflection. Both agents enhanced users' intentions to practice mindfulness. However, only the information-providing LLM, featuring a friendly persona, significantly improved engagement with the exercises. Our findings suggest that specific LLM agents may bridge the social support gap in digital health interventions.

Related papers

• Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation [51.44040615856536]
  This paper analyzes large language models' ability to simulate social media engagement through action guided response generation. We benchmark GPT-4o-mini, O1-mini, and DeepSeek-R1 in social media engagement simulation regarding a major societal event.
  arXiv  Detail & Related papers  (2025-02-17T17:43:08Z)
• MEETING DELEGATE: Benchmarking LLMs on Attending Meetings on Our Behalf [31.889028210500165]
  Large Language Models (LLMs) have demonstrated their strong capabilities in natural language generation and reasoning. We develop a prototype LLM-powered meeting delegate system and create a benchmark using real meeting transcripts. GPT-4/4o maintain balanced performance between active and cautious engagement strategies. Gemini 1.5 Pro tends to be more cautious, while Gemini 1.5 Flash and Llama3-8B/70B display more active tendencies.
  arXiv  Detail & Related papers  (2025-02-05T16:25:43Z)
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
  Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-04T17:26:58Z)
• Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media [22.976609127865732]
  Large language models (LLMs) are adept at adapting new tasks through few-shot learning. Our research explores LLMs' potential to assist human annotators in identifying morality frames within vaccination debates on social media.
  arXiv  Detail & Related papers  (2025-02-04T04:10:23Z)
• Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant [15.736792988697664]
  Large language models (LLMs) have continued to impact our everyday lives. Recent work has highlighted the value of 'LLM-modulo' setups in conjunction with humans-in-the-loop for planning tasks. We analyzed how user involvement at each stage affects their trust and collaborative team performance.
  arXiv  Detail & Related papers  (2025-02-03T14:23:22Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Evaluating Cultural and Social Awareness of LLM Web Agents [113.49968423990616]
  We introduce CASA, a benchmark designed to assess large language models' sensitivity to cultural and social norms. Our approach evaluates LLM agents' ability to detect and appropriately respond to norm-violating user queries and observations. Experiments show that current LLMs perform significantly better in non-agent environments.
  arXiv  Detail & Related papers  (2024-10-30T17:35:44Z)
• I Want to Break Free! Persuasion and Anti-Social Behavior of LLMs in Multi-Agent Settings with Social Hierarchy [13.68625980741047]
  We study interaction patterns of Large Language Model (LLM)-based agents in a context characterized by strict social hierarchy. We study two types of phenomena: persuasion and anti-social behavior in simulated scenarios involving a guard and a prisoner agent.
  arXiv  Detail & Related papers  (2024-10-09T17:45:47Z)
• Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms [7.550701021850185]
  We investigate the potential of Large Language Models (LLMs) to help students engage in post-lesson reflection. We conducted two randomized field experiments in undergraduate computer science courses.
  arXiv  Detail & Related papers  (2024-06-01T02:41:59Z)
• SOTOPIA-$π$: Interactive Learning of Socially Intelligent Language Agents [73.35393511272791]
  We propose an interactive learning method, SOTOPIA-$pi$, improving the social intelligence of language agents. This method leverages behavior cloning and self-reinforcement training on filtered social interaction data according to large language model (LLM) ratings.
  arXiv  Detail & Related papers  (2024-03-13T17:17:48Z)
• Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
  This study aims to determine the reliability of applying personality assessments to Large Language Models. Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
  arXiv  Detail & Related papers  (2023-05-31T15:03:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.