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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.12241">arXiv:2404.12241</a> <span> [<a href="https://arxiv.org/pdf/2404.12241">pdf</a>, <a href="https://arxiv.org/format/2404.12241">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Introducing v0.5 of the AI Safety Benchmark from MLCommons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vidgen%2C+B">Bertie Vidgen</a>, <a href="/search/cs?searchtype=author&query=Agrawal%2C+A">Adarsh Agrawal</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+A+M">Ahmed M. Ahmed</a>, <a href="/search/cs?searchtype=author&query=Akinwande%2C+V">Victor Akinwande</a>, <a href="/search/cs?searchtype=author&query=Al-Nuaimi%2C+N">Namir Al-Nuaimi</a>, <a href="/search/cs?searchtype=author&query=Alfaraj%2C+N">Najla Alfaraj</a>, <a href="/search/cs?searchtype=author&query=Alhajjar%2C+E">Elie Alhajjar</a>, <a href="/search/cs?searchtype=author&query=Aroyo%2C+L">Lora Aroyo</a>, <a href="/search/cs?searchtype=author&query=Bavalatti%2C+T">Trupti Bavalatti</a>, <a href="/search/cs?searchtype=author&query=Bartolo%2C+M">Max Bartolo</a>, <a href="/search/cs?searchtype=author&query=Blili-Hamelin%2C+B">Borhane Blili-Hamelin</a>, <a href="/search/cs?searchtype=author&query=Bollacker%2C+K">Kurt Bollacker</a>, <a href="/search/cs?searchtype=author&query=Bomassani%2C+R">Rishi Bomassani</a>, <a href="/search/cs?searchtype=author&query=Boston%2C+M+F">Marisa Ferrara Boston</a>, <a href="/search/cs?searchtype=author&query=Campos%2C+S">Sim茅on Campos</a>, <a href="/search/cs?searchtype=author&query=Chakra%2C+K">Kal Chakra</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+C">Canyu Chen</a>, <a href="/search/cs?searchtype=author&query=Coleman%2C+C">Cody Coleman</a>, <a href="/search/cs?searchtype=author&query=Coudert%2C+Z+D">Zacharie Delpierre Coudert</a>, <a href="/search/cs?searchtype=author&query=Derczynski%2C+L">Leon Derczynski</a>, <a href="/search/cs?searchtype=author&query=Dutta%2C+D">Debojyoti Dutta</a>, <a href="/search/cs?searchtype=author&query=Eisenberg%2C+I">Ian Eisenberg</a>, <a href="/search/cs?searchtype=author&query=Ezick%2C+J">James Ezick</a>, <a href="/search/cs?searchtype=author&query=Frase%2C+H">Heather Frase</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Brian Fuller</a> , et al. (75 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.12241v2-abstract-short" style="display: inline;"> 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-pu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.12241v2-abstract-full').style.display = 'inline'; document.getElementById('2404.12241v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.12241v2-abstract-full" style="display: none;"> 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. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.12241v2-abstract-full').style.display = 'none'; document.getElementById('2404.12241v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.13028">arXiv:2311.13028</a> <span> [<a href="https://arxiv.org/pdf/2311.13028">pdf</a>, <a href="https://arxiv.org/format/2311.13028">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> DMLR: Data-centric Machine Learning Research -- Past, Present and Future </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Oala%2C+L">Luis Oala</a>, <a href="/search/cs?searchtype=author&query=Maskey%2C+M">Manil Maskey</a>, <a href="/search/cs?searchtype=author&query=Bat-Leah%2C+L">Lilith Bat-Leah</a>, <a href="/search/cs?searchtype=author&query=Parrish%2C+A">Alicia Parrish</a>, <a href="/search/cs?searchtype=author&query=G%C3%BCrel%2C+N+M">Nezihe Merve G眉rel</a>, <a href="/search/cs?searchtype=author&query=Kuo%2C+T">Tzu-Sheng Kuo</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/cs?searchtype=author&query=Dror%2C+R">Rotem Dror</a>, <a href="/search/cs?searchtype=author&query=Brajovic%2C+D">Danilo Brajovic</a>, <a href="/search/cs?searchtype=author&query=Yao%2C+X">Xiaozhe Yao</a>, <a href="/search/cs?searchtype=author&query=Bartolo%2C+M">Max Bartolo</a>, <a href="/search/cs?searchtype=author&query=Rojas%2C+W+A+G">William A Gaviria Rojas</a>, <a href="/search/cs?searchtype=author&query=Hileman%2C+R">Ryan Hileman</a>, <a href="/search/cs?searchtype=author&query=Aliment%2C+R">Rainier Aliment</a>, <a href="/search/cs?searchtype=author&query=Mahoney%2C+M+W">Michael W. Mahoney</a>, <a href="/search/cs?searchtype=author&query=Risdal%2C+M">Meg Risdal</a>, <a href="/search/cs?searchtype=author&query=Lease%2C+M">Matthew Lease</a>, <a href="/search/cs?searchtype=author&query=Samek%2C+W">Wojciech Samek</a>, <a href="/search/cs?searchtype=author&query=Dutta%2C+D">Debojyoti Dutta</a>, <a href="/search/cs?searchtype=author&query=Northcutt%2C+C+G">Curtis G Northcutt</a>, <a href="/search/cs?searchtype=author&query=Coleman%2C+C">Cody Coleman</a>, <a href="/search/cs?searchtype=author&query=Hancock%2C+B">Braden Hancock</a>, <a href="/search/cs?searchtype=author&query=Koch%2C+B">Bernard Koch</a>, <a href="/search/cs?searchtype=author&query=Tadesse%2C+G+A">Girmaw Abebe Tadesse</a>, <a href="/search/cs?searchtype=author&query=Karla%C5%A1%2C+B">Bojan Karla拧</a> , et al. (13 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.13028v2-abstract-short" style="display: inline;"> Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science. We chart a path forward as a collective effort to sustain the creation and maintenance of these datasets and methods tow… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.13028v2-abstract-full').style.display = 'inline'; document.getElementById('2311.13028v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.13028v2-abstract-full" style="display: none;"> Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science. We chart a path forward as a collective effort to sustain the creation and maintenance of these datasets and methods towards positive scientific, societal and business impact. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.13028v2-abstract-full').style.display = 'none'; document.getElementById('2311.13028v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published in the Journal of Data-centric Machine Learning Research (DMLR) at https://data.mlr.press/assets/pdf/v01-5.pdf</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.16787">arXiv:2310.16787</a> <span> [<a href="https://arxiv.org/pdf/2310.16787">pdf</a>, <a href="https://arxiv.org/format/2310.16787">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Longpre%2C+S">Shayne Longpre</a>, <a href="/search/cs?searchtype=author&query=Mahari%2C+R">Robert Mahari</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+A">Anthony Chen</a>, <a href="/search/cs?searchtype=author&query=Obeng-Marnu%2C+N">Naana Obeng-Marnu</a>, <a href="/search/cs?searchtype=author&query=Sileo%2C+D">Damien Sileo</a>, <a href="/search/cs?searchtype=author&query=Brannon%2C+W">William Brannon</a>, <a href="/search/cs?searchtype=author&query=Muennighoff%2C+N">Niklas Muennighoff</a>, <a href="/search/cs?searchtype=author&query=Khazam%2C+N">Nathan Khazam</a>, <a href="/search/cs?searchtype=author&query=Kabbara%2C+J">Jad Kabbara</a>, <a href="/search/cs?searchtype=author&query=Perisetla%2C+K">Kartik Perisetla</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+X">Xinyi Wu</a>, <a href="/search/cs?searchtype=author&query=Shippole%2C+E">Enrico Shippole</a>, <a href="/search/cs?searchtype=author&query=Bollacker%2C+K">Kurt Bollacker</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+T">Tongshuang Wu</a>, <a href="/search/cs?searchtype=author&query=Villa%2C+L">Luis Villa</a>, <a href="/search/cs?searchtype=author&query=Pentland%2C+S">Sandy Pentland</a>, <a href="/search/cs?searchtype=author&query=Hooker%2C+S">Sara Hooker</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.16787v3-abstract-short" style="display: inline;"> The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tool… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.16787v3-abstract-full').style.display = 'inline'; document.getElementById('2310.16787v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.16787v3-abstract-full" style="display: none;"> The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tools and standards to trace the lineage of these datasets, from their source, creators, series of license conditions, properties, and subsequent use. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets, with closed datasets monopolizing important categories: lower resource languages, more creative tasks, richer topic variety, newer and more synthetic training data. This points to a deepening divide in the types of data that are made available under different license conditions, and heightened implications for jurisdictional legal interpretations of copyright and fair use. We also observe frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 70%+ and error rates of 50%+. This points to a crisis in misattribution and informed use of the most popular datasets driving many recent breakthroughs. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire audit, with an interactive UI, the Data Provenance Explorer, which allows practitioners to trace and filter on data provenance for the most popular open source finetuning data collections: www.dataprovenance.org. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.16787v3-abstract-full').style.display = 'none'; document.getElementById('2310.16787v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">30 pages (18 main), 6 figures, 5 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.15710">arXiv:2308.15710</a> <span> [<a href="https://arxiv.org/pdf/2308.15710">pdf</a>, <a href="https://arxiv.org/ps/2308.15710">ps</a>, <a href="https://arxiv.org/format/2308.15710">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Speech Wikimedia: A 77 Language Multilingual Speech Dataset </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=G%C3%B3mez%2C+R+M">Rafael Mosquera G贸mez</a>, <a href="/search/cs?searchtype=author&query=Eusse%2C+J">Juli谩n Eusse</a>, <a href="/search/cs?searchtype=author&query=Ciro%2C+J">Juan Ciro</a>, <a href="/search/cs?searchtype=author&query=Galvez%2C+D">Daniel Galvez</a>, <a href="/search/cs?searchtype=author&query=Hileman%2C+R">Ryan Hileman</a>, <a href="/search/cs?searchtype=author&query=Bollacker%2C+K">Kurt Bollacker</a>, <a href="/search/cs?searchtype=author&query=Kanter%2C+D">David Kanter</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.15710v1-abstract-short" style="display: inline;"> The Speech Wikimedia Dataset is a publicly available compilation of audio with transcriptions extracted from Wikimedia Commons. It includes 1780 hours (195 GB) of CC-BY-SA licensed transcribed speech from a diverse set of scenarios and speakers, in 77 different languages. Each audio file has one or more transcriptions in different languages, making this dataset suitable for training speech recogni… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.15710v1-abstract-full').style.display = 'inline'; document.getElementById('2308.15710v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.15710v1-abstract-full" style="display: none;"> The Speech Wikimedia Dataset is a publicly available compilation of audio with transcriptions extracted from Wikimedia Commons. It includes 1780 hours (195 GB) of CC-BY-SA licensed transcribed speech from a diverse set of scenarios and speakers, in 77 different languages. Each audio file has one or more transcriptions in different languages, making this dataset suitable for training speech recognition, speech translation, and machine translation models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.15710v1-abstract-full').style.display = 'none'; document.getElementById('2308.15710v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Data-Centric Machine Learning Workshop at the International Machine Learning Conference 2023 (ICML)</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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