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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/2402.04249">arXiv:2402.04249</a> <span> [<a href="https://arxiv.org/pdf/2402.04249">pdf</a>, <a href="https://arxiv.org/format/2402.04249">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="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mazeika%2C+M">Mantas Mazeika</a>, <a href="/search/cs?searchtype=author&query=Phan%2C+L">Long Phan</a>, <a href="/search/cs?searchtype=author&query=Yin%2C+X">Xuwang Yin</a>, <a href="/search/cs?searchtype=author&query=Zou%2C+A">Andy Zou</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zifan Wang</a>, <a href="/search/cs?searchtype=author&query=Mu%2C+N">Norman Mu</a>, <a href="/search/cs?searchtype=author&query=Sakhaee%2C+E">Elham Sakhaee</a>, <a href="/search/cs?searchtype=author&query=Li%2C+N">Nathaniel Li</a>, <a href="/search/cs?searchtype=author&query=Basart%2C+S">Steven Basart</a>, <a href="/search/cs?searchtype=author&query=Li%2C+B">Bo Li</a>, <a href="/search/cs?searchtype=author&query=Forsyth%2C+D">David Forsyth</a>, <a href="/search/cs?searchtype=author&query=Hendrycks%2C+D">Dan Hendrycks</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="2402.04249v2-abstract-short" style="display: inline;"> Automated red teaming holds substantial promise for uncovering and mitigating the risks associated with the malicious use of large language models (LLMs), yet the field lacks a standardized evaluation framework to rigorously assess new methods. To address this issue, we introduce HarmBench, a standardized evaluation framework for automated red teaming. We identify several desirable properties prev… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.04249v2-abstract-full').style.display = 'inline'; document.getElementById('2402.04249v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.04249v2-abstract-full" style="display: none;"> Automated red teaming holds substantial promise for uncovering and mitigating the risks associated with the malicious use of large language models (LLMs), yet the field lacks a standardized evaluation framework to rigorously assess new methods. To address this issue, we introduce HarmBench, a standardized evaluation framework for automated red teaming. We identify several desirable properties previously unaccounted for in red teaming evaluations and systematically design HarmBench to meet these criteria. Using HarmBench, we conduct a large-scale comparison of 18 red teaming methods and 33 target LLMs and defenses, yielding novel insights. We also introduce a highly efficient adversarial training method that greatly enhances LLM robustness across a wide range of attacks, demonstrating how HarmBench enables codevelopment of attacks and defenses. We open source HarmBench at https://github.com/centerforaisafety/HarmBench. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.04249v2-abstract-full').style.display = 'none'; document.getElementById('2402.04249v2-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> 26 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </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">Website: https://www.harmbench.org</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.03468">arXiv:2205.03468</a> <span> [<a href="https://arxiv.org/pdf/2205.03468">pdf</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> </div> </div> <p class="title is-5 mathjax"> The AI Index 2022 Annual Report </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+D">Daniel Zhang</a>, <a href="/search/cs?searchtype=author&query=Maslej%2C+N">Nestor Maslej</a>, <a href="/search/cs?searchtype=author&query=Brynjolfsson%2C+E">Erik Brynjolfsson</a>, <a href="/search/cs?searchtype=author&query=Etchemendy%2C+J">John Etchemendy</a>, <a href="/search/cs?searchtype=author&query=Lyons%2C+T">Terah Lyons</a>, <a href="/search/cs?searchtype=author&query=Manyika%2C+J">James Manyika</a>, <a href="/search/cs?searchtype=author&query=Ngo%2C+H">Helen Ngo</a>, <a href="/search/cs?searchtype=author&query=Niebles%2C+J+C">Juan Carlos Niebles</a>, <a href="/search/cs?searchtype=author&query=Sellitto%2C+M">Michael Sellitto</a>, <a href="/search/cs?searchtype=author&query=Sakhaee%2C+E">Ellie Sakhaee</a>, <a href="/search/cs?searchtype=author&query=Shoham%2C+Y">Yoav Shoham</a>, <a href="/search/cs?searchtype=author&query=Clark%2C+J">Jack Clark</a>, <a href="/search/cs?searchtype=author&query=Perrault%2C+R">Raymond Perrault</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="2205.03468v1-abstract-short" style="display: inline;"> Welcome to the fifth edition of the AI Index Report! The latest edition includes data from a broad set of academic, private, and nonprofit organizations as well as more self-collected data and original analysis than any previous editions, including an expanded technical performance chapter, a new survey of robotics researchers around the world, data on global AI legislation records in 25 countries… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.03468v1-abstract-full').style.display = 'inline'; document.getElementById('2205.03468v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.03468v1-abstract-full" style="display: none;"> Welcome to the fifth edition of the AI Index Report! The latest edition includes data from a broad set of academic, private, and nonprofit organizations as well as more self-collected data and original analysis than any previous editions, including an expanded technical performance chapter, a new survey of robotics researchers around the world, data on global AI legislation records in 25 countries, and a new chapter with an in-depth analysis of technical AI ethics metrics. The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The report aims to be the world's most credible and authoritative source for data and insights about AI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.03468v1-abstract-full').style.display = 'none'; document.getElementById('2205.03468v1-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> 2 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.13576">arXiv:2008.13576</a> <span> [<a href="https://arxiv.org/pdf/2008.13576">pdf</a>, <a href="https://arxiv.org/format/2008.13576">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TVCG.2020.3030394">10.1109/TVCG.2020.3030394 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Direct Volume Rendering with Nonparametric Models of Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Athawale%2C+T">Tushar Athawale</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+B">Bo Ma</a>, <a href="/search/cs?searchtype=author&query=Sakhaee%2C+E">Elham Sakhaee</a>, <a href="/search/cs?searchtype=author&query=Johnson%2C+C+R">Chris R. Johnson</a>, <a href="/search/cs?searchtype=author&query=Entezari%2C+A">Alireza Entezari</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="2008.13576v1-abstract-short" style="display: inline;"> We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statistical DVR framework allows for preserving the transfer function (TF) of the ground truth function when visualizing uncertain data; however, the existing framework is restricted to parametric models of uncertainty. In thi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.13576v1-abstract-full').style.display = 'inline'; document.getElementById('2008.13576v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.13576v1-abstract-full" style="display: none;"> We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statistical DVR framework allows for preserving the transfer function (TF) of the ground truth function when visualizing uncertain data; however, the existing framework is restricted to parametric models of uncertainty. In this paper, we address the limitations of the existing DVR framework by extending the DVR framework for nonparametric distributions. We exploit the quantile interpolation technique to derive probability distributions representing uncertainty in viewing-ray sample intensities in closed form, which allows for accurate and efficient computation. We evaluate our proposed nonparametric statistical models through qualitative and quantitative comparisons with the mean-field and parametric statistical models, such as uniform and Gaussian, as well as Gaussian mixtures. In addition, we present an extension of the state-of-the-art rendering parametric framework to 2D TFs for improved DVR classifications. We show the applicability of our uncertainty quantification framework to ensemble, downsampled, and bivariate versions of scalar field datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.13576v1-abstract-full').style.display = 'none'; document.getElementById('2008.13576v1-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> 31 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </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">11 pages,13 figures, accepted at the IEEE VIS 2020 conference</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 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