ArguMentor: Augmenting User Experiences with Counter-Perspectives
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915085980532736 |
|---|---|
| author | Pitre, Priya Luther, Kurt |
| author_facet | Pitre, Priya Luther, Kurt |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02795 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ArguMentor: Augmenting User Experiences with Counter-Perspectives Pitre, Priya Luther, Kurt Human-Computer Interaction Computation and Language 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. |
| title | ArguMentor: Augmenting User Experiences with Counter-Perspectives |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2406.02795 |