Summary of Walledeval: a Comprehensive Safety Evaluation Toolkit For Large Language Models, by Prannaya Gupta et al.
WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models
by Prannaya Gupta, Le Qi Yau, Hao Han Low, I-Shiang Lee, Hugo Maximus Lim, Yu Xin Teoh, Jia Hng Koh, Dar Win Liew, Rishabh Bhardwaj, Rajat Bhardwaj, Soujanya Poria
First submitted to arxiv on: 7 Aug 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 WalledEval is a comprehensive AI safety testing toolkit designed to evaluate large language models (LLMs). The framework accommodates various models, including open-weight and API-based ones, and features over 35 safety benchmarks covering areas such as multilingual safety, exaggerated safety, and prompt injections. WalledEval supports both LLM and judge benchmarking, incorporating custom mutators to test safety against text-style mutations like future tense and paraphrasing. Additionally, it introduces WalledGuard, a small content moderation tool, and two datasets: SGXSTest and HIXSTest, which serve as benchmarks for assessing exaggerated safety in cultural contexts. This toolkit provides a robust evaluation framework for LLMs, enabling developers to assess their performance and improve AI safety. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a special tool to test how safe artificial intelligence language models are. That’s what WalledEval is! It helps evaluate these powerful models by checking them against 35 different tests that cover many languages, making sure they don’t get too big for their britches. The tool also lets you test judges who make decisions based on the AI’s answers. WalledGuard is a new and fast way to keep content safe online. Two special datasets help us understand how well these tools work in different cultures. This toolkit helps people create better, safer AI models. |
Keywords
» Artificial intelligence » Prompt
