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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"> Discovering Language Model Behaviors with Model-Written Evaluations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&amp;query=Perez%2C+E">Ethan Perez</a>, <a href="/search/cs?searchtype=author&amp;query=Ringer%2C+S">Sam Ringer</a>, <a href="/search/cs?searchtype=author&amp;query=Luko%C5%A1i%C5%ABt%C4%97%2C+K">Kamil臈 Luko拧i奴t臈</a>, <a href="/search/cs?searchtype=author&amp;query=Nguyen%2C+K">Karina Nguyen</a>, <a href="/search/cs?searchtype=author&amp;query=Chen%2C+E">Edwin Chen</a>, <a href="/search/cs?searchtype=author&amp;query=Heiner%2C+S">Scott Heiner</a>, <a href="/search/cs?searchtype=author&amp;query=Pettit%2C+C">Craig Pettit</a>, <a href="/search/cs?searchtype=author&amp;query=Olsson%2C+C">Catherine Olsson</a>, <a href="/search/cs?searchtype=author&amp;query=Kundu%2C+S">Sandipan Kundu</a>, <a href="/search/cs?searchtype=author&amp;query=Kadavath%2C+S">Saurav Kadavath</a>, <a href="/search/cs?searchtype=author&amp;query=Jones%2C+A">Andy Jones</a>, <a href="/search/cs?searchtype=author&amp;query=Chen%2C+A">Anna Chen</a>, <a href="/search/cs?searchtype=author&amp;query=Mann%2C+B">Ben Mann</a>, <a href="/search/cs?searchtype=author&amp;query=Israel%2C+B">Brian Israel</a>, <a href="/search/cs?searchtype=author&amp;query=Seethor%2C+B">Bryan Seethor</a>, <a href="/search/cs?searchtype=author&amp;query=McKinnon%2C+C">Cameron McKinnon</a>, <a href="/search/cs?searchtype=author&amp;query=Olah%2C+C">Christopher Olah</a>, <a href="/search/cs?searchtype=author&amp;query=Yan%2C+D">Da Yan</a>, <a href="/search/cs?searchtype=author&amp;query=Amodei%2C+D">Daniela Amodei</a>, <a href="/search/cs?searchtype=author&amp;query=Amodei%2C+D">Dario Amodei</a>, <a href="/search/cs?searchtype=author&amp;query=Drain%2C+D">Dawn Drain</a>, <a href="/search/cs?searchtype=author&amp;query=Li%2C+D">Dustin Li</a>, <a href="/search/cs?searchtype=author&amp;query=Tran-Johnson%2C+E">Eli Tran-Johnson</a>, <a href="/search/cs?searchtype=author&amp;query=Khundadze%2C+G">Guro Khundadze</a>, <a href="/search/cs?searchtype=author&amp;query=Kernion%2C+J">Jackson Kernion</a> , et al. (38 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="2212.09251v1-abstract-short" style="display: inline;"> As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from inst&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.09251v1-abstract-full').style.display = 'inline'; document.getElementById('2212.09251v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.09251v1-abstract-full" style="display: none;"> As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user&#39;s preferred answer (&#34;sycophancy&#34;) and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.09251v1-abstract-full').style.display = 'none'; document.getElementById('2212.09251v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </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">for associated data visualizations, see https://www.evals.anthropic.com/model-written/ for full datasets, see https://github.com/anthropics/evals</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.08491">arXiv:2203.08491</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2203.08491">pdf</a>, <a href="https://arxiv.org/format/2203.08491">other</a>]&nbsp;</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="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Deepchecks: A Library for Testing and Validating Machine Learning Models and Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&amp;query=Chorev%2C+S">Shir Chorev</a>, <a href="/search/cs?searchtype=author&amp;query=Tannor%2C+P">Philip Tannor</a>, <a href="/search/cs?searchtype=author&amp;query=Israel%2C+D+B">Dan Ben Israel</a>, <a href="/search/cs?searchtype=author&amp;query=Bressler%2C+N">Noam Bressler</a>, <a href="/search/cs?searchtype=author&amp;query=Gabbay%2C+I">Itay Gabbay</a>, <a href="/search/cs?searchtype=author&amp;query=Hutnik%2C+N">Nir Hutnik</a>, <a href="/search/cs?searchtype=author&amp;query=Liberman%2C+J">Jonatan Liberman</a>, <a href="/search/cs?searchtype=author&amp;query=Perlmutter%2C+M">Matan Perlmutter</a>, <a href="/search/cs?searchtype=author&amp;query=Romanyshyn%2C+Y">Yurii Romanyshyn</a>, <a href="/search/cs?searchtype=author&amp;query=Rokach%2C+L">Lior Rokach</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="2203.08491v1-abstract-short" style="display: inline;"> This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data. Our goal is to provide an easy-to-use library comprising of many checks related to various types of issues, such as model predictive performance, data integrity, data distribution mismatches, and more. The package is distributed under the GNU Affero General Public License (AGPL) and re&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.08491v1-abstract-full').style.display = 'inline'; document.getElementById('2203.08491v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.08491v1-abstract-full" style="display: none;"> This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data. Our goal is to provide an easy-to-use library comprising of many checks related to various types of issues, such as model predictive performance, data integrity, data distribution mismatches, and more. The package is distributed under the GNU Affero General Public License (AGPL) and relies on core libraries from the scientific Python ecosystem: scikit-learn, PyTorch, NumPy, pandas, and SciPy. Source code, documentation, examples, and an extensive user guide can be found at \url{https://github.com/deepchecks/deepchecks} and \url{https://docs.deepchecks.com/}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.08491v1-abstract-full').style.display = 'none'; document.getElementById('2203.08491v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>&nbsp;&nbsp;</span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary"> <!-- MetaColumn 1 --> 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