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📣 I'm thrilled to announce "ALERT: A Comprehensive #Benchmark for Assessing #LLMs’ Safety through #RedTeaming" 🚨
📣 I'm thrilled to announce "ALERT: A Comprehensive #Benchmark for Assessing #LLMs’ Safety through #RedTeaming" 🚨
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📄 Paper: 
📄 Paper: 
link
https://arxiv.org/pdf/2404.08676.pdf
https://arxiv.org/pdf/2404.08676.pdf
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🗃️ Repo: 
🗃️ Repo: 
link
https://github.com/Babelscape/ALERT
https://github.com/Babelscape/ALERT
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🤗 ALERT benchmark: 
🤗 ALERT benchmark: 
resource
https://huggingface.co/datasets/Babelscape/ALERT
dataset
Babelscape/ALERT
https://huggingface.co/datasets/Babelscape/ALERT
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🤗 ALERT DPO data: 
🤗 ALERT DPO data: 
resource
https://huggingface.co/datasets/Babelscape/ALERT_DPO
dataset
Babelscape/ALERT_DPO
https://huggingface.co/datasets/Babelscape/ALERT_DPO
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As a key design principle for ALERT, we developed a fine-grained safety risk taxonomy (Fig. 2). This taxonomy serves as the foundation for the benchmark to provide detailed insights about a model’s weaknesses and vulnerabilities as well as inform targeted safety enhancements 🛡️
As a key design principle for ALERT, we developed a fine-grained safety risk taxonomy (Fig. 2). This taxonomy serves as the foundation for the benchmark to provide detailed insights about a model’s weaknesses and vulnerabilities as well as inform targeted safety enhancements 🛡️
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For collecting our prompts, we started from the popular 
For collecting our prompts, we started from the popular 
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Anthropic's HH-RLHF data, and used automated strategies to filter/classify prompts. We then designed templates to create new prompts (providing sufficient support for each category, cf. Fig. 3) and implemented adversarial attacks.
Anthropic's HH-RLHF data, and used automated strategies to filter/classify prompts. We then designed templates to create new prompts (providing sufficient support for each category, cf. Fig. 3) and implemented adversarial attacks.
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In our experiments, we extensively evaluated several open- and closed-source LLMs (e.g. #ChatGPT, #Llama and #Mistral), highlighting their strengths and weaknesses (Table 1).
In our experiments, we extensively evaluated several open- and closed-source LLMs (e.g. #ChatGPT, #Llama and #Mistral), highlighting their strengths and weaknesses (Table 1).
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For more details, check out our preprint: 
For more details, check out our preprint: 
link
https://arxiv.org/pdf/2404.08676.pdf
https://arxiv.org/pdf/2404.08676.pdf
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 🤓
 🤓
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Huge thanks to 
Huge thanks to 
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@felfri
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, 
, 
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@PSaiml
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, Kristian Kersting, 
, Kristian Kersting, 
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@navigli
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, 
, 
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@huu-ontocord
huu-ontocord
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 and 
 and 
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@BoLi-aisecure
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 (and all the organizations involved: Babelscape, Sapienza NLP, TU Darmstadt, Hessian.AI, DFKI, Ontocord.AI, UChicago and UIUC)🫂
 (and all the organizations involved: Babelscape, Sapienza NLP, TU Darmstadt, Hessian.AI, DFKI, Ontocord.AI, UChicago and UIUC)🫂
📣 I'm thrilled to announce "ALERT: A Comprehensive #Benchmark for Assessing #LLMs’ Safety through #RedTeaming" 🚨

📄 Paper: https://arxiv.org/pdf/2404.08676.pdf
🗃️ Repo: https://github.com/Babelscape/ALERT
🤗 ALERT benchmark: https://huggingface.co/datasets/Babelscape/ALERT
🤗 ALERT DPO data: https://huggingface.co/datasets/Babelscape/ALERT_DPO

As a key design principle for ALERT, we developed a fine-grained safety risk taxonomy (Fig. 2). This taxonomy serves as the foundation for the benchmark to provide detailed insights about a model’s weaknesses and vulnerabilities as well as inform targeted safety enhancements 🛡️

For collecting our prompts, we started from the popular 
Anthropic's HH-RLHF data, and used automated strategies to filter/classify prompts. We then designed templates to create new prompts (providing sufficient support for each category, cf. Fig. 3) and implemented adversarial attacks.

In our experiments, we extensively evaluated several open- and closed-source LLMs (e.g. #ChatGPT, #Llama and #Mistral), highlighting their strengths and weaknesses (Table 1).

For more details, check out our preprint: https://arxiv.org/pdf/2404.08676.pdf 🤓

Huge thanks to @felfri, @PSaiml, Kristian Kersting, @navigli, @huu-ontocord and @BoLi-aisecure (and all the organizations involved: Babelscape, Sapienza NLP, TU Darmstadt, Hessian.AI, DFKI, Ontocord.AI, UChicago and UIUC)🫂
🔥
victor clefourrier osanseviero BrigitteTousi lunarflu huu-ontocord Dlbk tomaarsen AdinaY KvrParaskevi eliolio vicgalle kklyman taufiqdp Vlansu riccorl
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felfri lunarflu huu-ontocord Dlbk clem tomaarsen
❤️
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Adam Molnar
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