Introducing v05 of the AI Safety Benchmark from MLCommons
<|reference_start|>Introducing v05 of the AI Safety Benchmark from MLCommons: This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.<|reference_end|>
@article{vidgen2024introducing,
    title={Introducing v0.5 of the AI Safety Benchmark from MLCommons},
    author={Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir
  Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Max
  Bartolo, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa
  Ferrara Boston, Sim'eon Campos, Kal Chakra, Canyu Chen, Cody Coleman,
  Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg,
  James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya
  Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman
  Gohar, Sujata Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial,
  Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse Khomh, Bhavya Kailkhura,
  Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H.
  Kumar, Srijan Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long,
  Victor Lu, Sarah Luger, Yifan Mai, Priyanka Mary Mammen, Kelvin Manyeki, Sean
  McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh
  Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr,
  Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh,
  Eleonora Presani, Fabrizio Puletti, Paul R"ottger, Saurav Sahay, Tim Santos,
  Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl
  Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide
  Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss,
  Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav,
  Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy
  Liang, Peter Mattson, Joaquin Vanschoren},
    journal={arXiv preprint arXiv:2404.12241},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12241},
    primaryClass={cs.CL cs.AI}
}