# ArguMentor: Augmenting User Experiences with Counter-Perspectives

arXiv: https://arxiv.org/abs/2406.02795 · HTML: https://mlsanity.com/paper/2406.02795/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://mlsanity.com/paper/2310.03693/) (2023)
- [Debating with More Persuasive LLMs Leads to More Truthful Answers](https://mlsanity.com/paper/2402.06782/) (2024)
- [Scim: Intelligent Skimming Support for Scientific Papers](https://mlsanity.com/paper/2205.04561/) (2022)
- [Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool](https://mlsanity.com/paper/2207.08401/) (2022)
- [Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions](https://mlsanity.com/paper/2302.08997/) (2023)

## Abstract (arXiv)

We encounter arguments everyday in the form of social media posts, presidential debates, news articles, and even advertisements. A ubiquitous, influential example is the opinion piece (op-ed). Opinion pieces can provide valuable perspectives, but they often represent only one side of a story, which can make readers susceptible to confirmation bias and echo chambers. Exposure to different perspectives can help readers overcome these obstacles and form more robust, nuanced views on important societal issues. We designed ArguMentor, a human-AI collaboration system that highlights claims in opinion pieces, identifies counter-arguments for them using a LLM, and generates a context-based summary of based on current events. It further enhances user understanding through additional features like a Q&A bot (that answers user questions pertaining to the text), DebateMe (an agent that users can argue any side of the piece with) and highlighting (where users can highlight a word or passage to get its definition or context). Our evaluation on news op-eds shows that participants can generate more arguments and counter-arguments and display higher critical thinking skills after engaging with the system. Further discussion highlights a more general need for this kind of a system.