Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot

• URL: http://arxiv.org/abs/2505.09806v1
• Date: Wed, 14 May 2025 21:16:07 GMT
• Title: Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot
• Authors: Jianlong Zhu, Manon Kempermann, Vikram Kamath Cannanure, Alexander Hartland, Rosa M. Navarrete, Giuseppe Carteny, Daniela Braun, Ingmar Weber,
• Abstract summary: Voting advice applications (VAAs) are seen as a successful tool for boosting electorates' political knowledge and engagement.<n>However, VAAs' complex language and rigid presentation constrain their utility to less-sophisticated voters.<n>This study investigates how conversational chatbots can support voting preparation.
• Score: 35.20807577857132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Voting advice applications (VAAs), which have become increasingly prominent in European elections, are seen as a successful tool for boosting electorates' political knowledge and engagement. However, VAAs' complex language and rigid presentation constrain their utility to less-sophisticated voters. While previous work enhanced VAAs' click-based interaction with scripted explanations, a conversational chatbot's potential for tailored discussion and deliberate political decision-making remains untapped. Our exploratory mixed-method study investigates how LLM-based chatbots can support voting preparation. We deployed a VAA chatbot to 331 users before Germany's 2024 European Parliament election, gathering insights from surveys, conversation logs, and 10 follow-up interviews. Participants found the VAA chatbot intuitive and informative, citing its simple language and flexible interaction. We further uncovered VAA chatbots' role as a catalyst for reflection and rationalization. Expanding on participants' desire for transparency, we provide design recommendations for building interactive and trustworthy VAA chatbots.

Related papers

• Prompt Engineering a Schizophrenia Chatbot: Utilizing a Multi-Agent Approach for Enhanced Compliance with Prompt Instructions [0.0699049312989311]
  Patients with schizophrenia often present with cognitive impairments that may hinder their ability to learn about their condition. While Large Language Models (LLMs) have the potential to make topical mental health information more accessible and engaging, their black-box nature raises concerns about ethics and safety.
  arXiv  Detail & Related papers  (2024-10-10T09:49:24Z)
• AVIN-Chat: An Audio-Visual Interactive Chatbot System with Emotional State Tuning [9.989693906734535]
  AVIN-Chat allows users to have face-to-face conversations with 3D avatars in real-time. The proposed AVIN-Chat emotionally speaks and expresses according to the user's emotional state.
  arXiv  Detail & Related papers  (2024-08-15T22:45:53Z)
• KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces [55.00702535694059]
  KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI.
  arXiv  Detail & Related papers  (2024-04-22T15:01:39Z)
• Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference [48.99117537559644]
  We introduce Arena, an open platform for evaluating Large Language Models (LLMs) based on human preferences. Our methodology employs a pairwise comparison approach and leverages input from a diverse user base through crowdsourcing. This paper describes the platform, analyzes the data we have collected so far, and explains the tried-and-true statistical methods we are using.
  arXiv  Detail & Related papers  (2024-03-07T01:22:38Z)
• In Generative AI we Trust: Can Chatbots Effectively Verify Political Information? [39.58317527488534]
  This article presents a comparative analysis of the ability of two large language model (LLM)-based chatbots, ChatGPT and Bing Chat, to detect veracity of political information. We use AI auditing methodology to investigate how chatbots evaluate true, false, and borderline statements on five topics: COVID-19, Russian aggression against Ukraine, the Holocaust, climate change, and LGBTQ+ related debates. The results show high performance of ChatGPT for the baseline veracity evaluation task, with 72 percent of the cases evaluated correctly on average across languages without pre-training.
  arXiv  Detail & Related papers  (2023-12-20T15:17:03Z)
• On Safe and Usable Chatbots for Promoting Voter Participation [8.442334707366173]
  We build a system that amplifies official information while personalizing it to users' unique needs. Our approach can be a win-win for voters, election agencies trying to fulfill their mandate and democracy at large.
  arXiv  Detail & Related papers  (2022-12-16T08:07:51Z)
• EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments [75.11753644302385]
  Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the implied feelings of the conversation partner. We propose a method based on a transformer pretrained language model (T5) We evaluate our model on the EmpatheticDialogues dataset using both automated metrics and human evaluation.
  arXiv  Detail & Related papers  (2021-10-30T19:04:48Z)
• CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement Learning [60.348822346249854]
  This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns. We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning. To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy.
  arXiv  Detail & Related papers  (2021-10-08T07:44:47Z)
• Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention [55.77218465471519]
  This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework included a guiding robot and an interlocutor model that plays the role of humans. We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
  arXiv  Detail & Related papers  (2021-03-30T15:24:37Z)

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.