Summary of Realistic Evaluation Of Toxicity in Large Language Models, by Tinh Son Luong et al.
Realistic Evaluation of Toxicity in Large Language Models
by Tinh Son Luong, Thanh-Thien Le, Linh Ngo Van, Thien Huu Nguyen
First submitted to arxiv on: 17 May 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 approach to evaluating large language models’ (LLMs) ability to recognize and generate toxic content is presented. The Thoroughly Engineered Toxicity (TET) dataset, comprising manually crafted prompts, is designed to bypass the protective layers of LLMs and reveal subtleties in their behavior. Evaluations demonstrate TET’s effectiveness in identifying toxicity in popular LLMs, highlighting potential issues in their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have become a big part of our daily lives. While they’re very good at understanding lots of things, they can also be exposed to bad or unfair information. To help keep them from generating this kind of content, some models use special defenses. However, these defenses can be easily tricked with just a little bit of creative writing. In this paper, scientists create a new dataset called Thoroughly Engineered Toxicity (TET). It’s made up of prompts that are specifically designed to get around the defenses of language models. By testing TET on some popular models, researchers show how it can help identify when these models might not be as good at recognizing bad content as they should be. |
