Summary of Assessing Good, Bad and Ugly Arguments Generated by Chatgpt: a New Dataset, Its Methodology and Associated Tasks, By Victor Hugo Nascimento Rocha et al.
Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks
by Victor Hugo Nascimento Rocha, Igor Cataneo Silveira, Paulo Pirozelli, Denis Deratani Mauá, Fabio Gagliardi Cozman
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel methodology is introduced to generate good, bad, and ugly arguments from ChatGPT’s output, addressing concerns about Large Language Models (LLMs) spreading misinformation. The approach yields ArGPT, a dataset of diverse arguments, which is assessed for its effectiveness in several argumentation-related tasks. Baselines are established for these tasks, demonstrating the utility of artificially generated data as a tool to train and test systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to create examples of good and bad arguments from chatbots like ChatGPT is developed. This helps solve a problem where language models might spread false information. The method produces a special set of texts with different types of arguments, which can be used to make tools that identify fake or real arguments. The dataset is tested and shown to work well in tasks related to arguing and debating. |
