Summary of Large Language Models Reflect the Ideology Of Their Creators, by Maarten Buyl et al.
Large Language Models Reflect the Ideology of their Creators
by Maarten Buyl, Alexander Rogiers, Sander Noels, Guillaume Bied, Iris Dominguez-Catena, Edith Heiter, Iman Johary, Alexandru-Cristian Mara, Raphaël Romero, Jefrey Lijffijt, Tijl De Bie
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The abstract discusses large language models (LLMs), trained on vast amounts of data to generate natural language for tasks like text summarization and question answering. These models have become popular in AI assistants like ChatGPT and play a significant role in how humans access information. However, the behavior of LLMs varies depending on their design, training, and use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are trained to generate natural language for tasks like text summarization and question answering. These models have become popular in AI assistants like ChatGPT and play a significant role in how humans access information. The paper explores the behavior of LLMs and how it varies depending on their design, training, and use. |
Keywords
» Artificial intelligence » Question answering » Summarization
