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href="/news">News</a></nav></div></header><main id="main" class=""><article><div class="wrapper PostDetail_wrapper__dZTNU"><div class="PostDetail_post-heading__dYlxy"><div class="PostDetail_post-detail-types-subjects__JnuwX"><span class="PostDetail_disabled__o0ozQ btn-secondary btn-chip">Societal Impacts</span></div><h1 class="h2">Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned</h1><div class="PostDetail_post-timestamp__4tN6D text-label">Aug 22, 2022</div><div class="PostDetail_cta-button__5Btay"><a href="https://arxiv.org/abs/2209.07858" class="ButtonLink_button__PxYMX ButtonLink_primary__K_u7f ButtonLink_small__yFWit" rel="noopener" target="_blank" aria-label="Read Paper"><span class="ButtonLink_label__W_hxv">Read Paper</span></a></div></div><div class="text-b2 PostDetail_post-detail__uTcjp PostDetail_with-related-links__KHoHR"><article><div><div class="ReadingDetail_detail__wf2_W"><h4 class="ReadingDetail_reading-column__FguxH post-subsection">Abstract</h4><p class="ReadingDetail_reading-column__FguxH post-text">We describe our early efforts to red team language models in order to simultaneously discover, measure, and attempt to reduce their potentially harmful outputs. We make three main contributions. First, we investigate scaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B parameters) and 4 model types: a plain language model (LM); an LM prompted to be helpful, honest, and harmless; an LM with rejection sampling; and a model trained to be helpful and harmless using reinforcement learning from human feedback (RLHF). We find that the RLHF models are increasingly difficult to red team as they scale, and we find a flat trend with scale for the other model types. Second, we release our dataset of 38,961 red team attacks for others to analyze and learn from. We provide our own analysis of the data and find a variety of harmful outputs, which range from offensive language to more subtly harmful non-violent unethical outputs. Third, we exhaustively describe our instructions, processes, statistical methodologies, and uncertainty about red teaming. We hope that this transparency accelerates our ability to work together as a community in order to develop shared norms, practices, and technical standards for how to red team language models.</p><h4 class="ReadingDetail_reading-column__FguxH post-subsection">Policy Memo</h4><p class="ReadingDetail_reading-column__FguxH post-text"><a href="https://www-cdn.anthropic.com/82564d4ec2451b2eed2e0796b7c658fc989f0c1a/Anthropic_RedTeaming.pdf">Red Teaming Policy Memo</a></p></div></div></article><div class="PostDetail_b-social-share__DBMfH"><a href="https://twitter.com/intent/tweet?text=https://www.anthropic.com/research/red-teaming-language-models-to-reduce-harms-methods-scaling-behaviors-and-lessons-learned" target="_blank" rel="noopener" aria-label="Share on Twitter"><svg class="Icon_icon__WRMkZ" width="24" height="24" viewBox="0 0 24 24"><path d="M18.244 2.25h3.308l-7.227 8.26 8.502 11.24H16.17l-5.214-6.817L4.99 21.75H1.68l7.73-8.835L1.254 2.25H8.08l4.713 6.231zm-1.161 17.52h1.833L7.084 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