Summary of Alert: a Comprehensive Benchmark For Assessing Large Language Models’ Safety Through Red Teaming, by Simone Tedeschi et al.
ALERT: A Comprehensive Benchmark for Assessing Large Language Models’ Safety through Red Teaming
by Simone Tedeschi, Felix Friedrich, Patrick Schramowski, Kristian Kersting, Roberto Navigli, Huu Nguyen, Bo Li
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); 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 This research paper introduces ALERT, a novel benchmark designed to assess the safety of Large Language Models (LLMs). The authors propose a fine-grained risk taxonomy to evaluate the safety of LLMs through adversarial testing scenarios. This framework aims to identify vulnerabilities, inform improvements, and enhance the overall safety of language models. The researchers conduct extensive experiments on 10 popular open- and closed-source LLMs, demonstrating that many struggle to achieve reasonable levels of safety. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper focuses on building safe Large Language Models (LLMs) by introducing ALERT, a benchmark that assesses their safety using a novel fine-grained risk taxonomy. This helps identify vulnerabilities and improves the overall safety of language models. The authors test 10 popular LLMs and show that many need improvement to achieve reasonable safety levels. |
