Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles

• URL: http://arxiv.org/abs/2407.00870v2
• Date: Sun, 14 Jul 2024 22:35:34 GMT
• Title: Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles
• Authors: Ryan Louie, Ananjan Nandi, William Fang, Cheng Chang, Emma Brunskill, Diyi Yang,
• Abstract summary: We develop Roleplay-doh, a novel human-LLM collaboration pipeline that elicits qualitative feedback from a domain-expert. We apply this pipeline to enable senior mental health supporters to create customized AI patients for simulated practice partners.
• Score: 58.82161879559716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works leverage LLMs to roleplay realistic social scenarios, aiding novices in practicing their social skills. However, simulating sensitive interactions, such as in mental health, is challenging. Privacy concerns restrict data access, and collecting expert feedback, although vital, is laborious. To address this, we develop Roleplay-doh, a novel human-LLM collaboration pipeline that elicits qualitative feedback from a domain-expert, which is transformed into a set of principles, or natural language rules, that govern an LLM-prompted roleplay. We apply this pipeline to enable senior mental health supporters to create customized AI patients for simulated practice partners for novice counselors. After uncovering issues in GPT-4 simulations not adhering to expert-defined principles, we also introduce a novel principle-adherence prompting pipeline which shows 30% improvements in response quality and principle following for the downstream task. Via a user study with 25 counseling experts, we demonstrate that the pipeline makes it easy and effective to create AI patients that more faithfully resemble real patients, as judged by creators and third-party counselors. See our project website at https://roleplay-doh.github.io/ for code and data.

Related papers

• ChainBuddy: An AI Agent System for Generating LLM Pipelines [2.7624021966289605]
  ChainBuddy is an AI assistant for generating evaluative pipelines built into the ChainForge platform. We report a within-subjects user study comparing ChainBuddy to the baseline interface. We find that when using AI assistance, participants reported a less demanding workload.
  arXiv  Detail & Related papers  (2024-09-20T15:42:33Z)
• Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions [12.455050661682051]
  We propose a framework that employs two large language models (LLMs) via role-playing for simulating counselor-client interactions. Our framework involves two LLMs, one acting as a client equipped with a specific and real-life user profile and the other playing the role of an experienced counselor.
  arXiv  Detail & Related papers  (2024-08-28T13:29:59Z)
• Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data [58.92110996840019]
  We propose to enhance role-playing language models (RPLMs) via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations.
  arXiv  Detail & Related papers  (2024-06-27T06:24:00Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• Can AI Relate: Testing Large Language Model Response for Mental Health Support [23.97212082563385]
  Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. We develop an evaluation framework for determining whether LLM response is a viable and ethical path forward for the automation of mental health treatment.
  arXiv  Detail & Related papers  (2024-05-20T13:42:27Z)
• Response Generation for Cognitive Behavioral Therapy with Large Language Models: Comparative Study with Socratic Questioning [6.400704401007114]
  This study investigates the impact of generated responses on subjective evaluations such as mood change, cognitive change, and dialogue quality. When using GPT-4, the amount of mood change, empathy, and other dialogue qualities improve significantly.
  arXiv  Detail & Related papers  (2024-01-29T08:53:41Z)
• Character-LLM: A Trainable Agent for Role-Playing [67.35139167985008]
  Large language models (LLMs) can be used to serve as agents to simulate human behaviors. We introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
  arXiv  Detail & Related papers  (2023-10-16T07:58:56Z)
• How Can Recommender Systems Benefit from Large Language Models: A Survey [82.06729592294322]
  Large language models (LLM) have shown impressive general intelligence and human-like capabilities. We conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
  arXiv  Detail & Related papers  (2023-06-09T11:31:50Z)
• Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision [84.31474052176343]
  Recent AI-assistant agents, such as ChatGPT, rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback to align the output with human intentions. This dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision. We propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision.
  arXiv  Detail & Related papers  (2023-05-04T17:59:28Z)

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.