Summary of Socio-culturally Aware Evaluation Framework For Llm-based Content Moderation, by Shanu Kumar et al.
Socio-Culturally Aware Evaluation Framework for LLM-Based Content Moderation
by Shanu Kumar, Gauri Kholkar, Saish Mendke, Anubhav Sadana, Parag Agrawal, Sandipan Dandapat
First submitted to arxiv on: 18 Dec 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 The proposed socio-culturally aware evaluation framework for large language model (LLM)-driven content moderation tackles the issue of unreliable assessments in existing datasets by lacking adequate representation of different groups. The framework introduces a scalable method for creating diverse datasets using persona-based generation, which provides broader perspectives and poses greater challenges for LLMs than diversity-focused methods without personas. This challenge is particularly pronounced in smaller LLMs, highlighting their difficulties in moderating diverse content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to evaluate how well large language models can be used to moderate online content. Right now, there are lots of datasets that don’t include enough examples from different groups, which makes it hard to trust the results. The authors suggest a new approach called “socio-culturally aware” that creates more diverse datasets by thinking about people’s personalities and backgrounds. This helps make sure that online content is moderated in a way that’s fair and reasonable. |
Keywords
» Artificial intelligence » Large language model
