Designing and Evaluating Multi-Chatbot Interface for Human-AI Communication: Preliminary Findings from a Persuasion Task

Sion Yoon; Tae Eun Kim; Yoo Jung Oh

Designing and Evaluating Multi-Chatbot Interface for Human-AI Communication: Preliminary Findings from a Persuasion Task

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2406.19648 v1 pith:VZYAPJHD submitted 2024-06-28 cs.HC cs.AIcs.CL

Sion Yoon , Tae Eun Kim , Yoo Jung Oh This is my paper · ORCID

classification cs.HCcs.AIcs.CL

keywords communicationchatbotsmulti-chatbothuman-aicharitabledonationsdynamicsexperiment

verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments

read the original abstract

The dynamics of human-AI communication have been reshaped by language models such as ChatGPT. However, extant research has primarily focused on dyadic communication, leaving much to be explored regarding the dynamics of human-AI communication in group settings. The availability of multiple language model chatbots presents a unique opportunity for scholars to better understand the interaction between humans and multiple chatbots. This study examines the impact of multi-chatbot communication in a specific persuasion setting: promoting charitable donations. We developed an online environment that enables multi-chatbot communication and conducted a pilot experiment utilizing two GPT-based chatbots, Save the Children and UNICEF chatbots, to promote charitable donations. In this study, we present our development process of the multi-chatbot interface and present preliminary findings from a pilot experiment. Analysis of qualitative and quantitative feedback are presented, and limitations are addressed.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration
cs.LG 2024-11 conditional novelty 6.0 of 10

DrugAgent, a Planner-Instructor multi-agent LLM framework with drug-discovery documentation, beats ReAct by 4.92% ROC-AUC on DTI and exceeds general agents on two of three tasks in its Top3 variant.

Pith tools