Summary of Large Language Models For Crowd Decision Making Based on Prompt Design Strategies Using Chatgpt: Models, Analysis and Challenges, by Cristina Zuheros and David Herrera-poyatos and Rosana Montes and Francisco Herrera
Large language models for crowd decision making based on prompt design strategies using ChatGPT: models, analysis and challenges
by Cristina Zuheros, David Herrera-Poyatos, Rosana Montes, Francisco Herrera
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the potential of Large Language Models (LLMs) like ChatGPT in Crowd Decision Making (CDM) processes. Specifically, it examines how prompt design strategies can be used to enable ChatGPT to assist in CDM tasks, such as opinion extraction and decision making. The study integrates ChatGPT into a multi-criteria decision making scenario with a category ontology for criteria, leveraging real-world data from TripAdvisor’s TripR-2020Large dataset. Results show promising potential for developing quality decision-making models using ChatGPT. However, the paper also highlights challenges related to consistency, sensitivity, and explainability when using LLMs in CDM processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how computers can help make decisions by analyzing what people say online. It uses a special AI model called ChatGPT to understand opinions expressed on social media platforms like TripAdvisor. The study shows that ChatGPT can be used to help make decisions by extracting opinions from text and providing scores for different options. The results are promising, but the researchers also discuss some challenges they found when using this kind of technology. |
Keywords
* Artificial intelligence * Prompt
