We’ll onboard people shortly after Christmas and shut off this form sometime before Christmas, or whenever it reaches our internal support capacity. We’re particularly excited to collectively come up with creative ways to find new features and problems with these models.
@anthropicai
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Anthropic Shares Constitutional AI Feedback Interface with Broader Audience
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Given the growing interest in language model-based chat interfaces, we’re sharing our Constitutional AI feedback interface with a larger set of people. Sign up here: https://
forms.gle/12FCefc6sHfBsP
9j9
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Language Models Augmenting Evaluation Authors for Faster Assessment
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We’re excited about the potential of LMs to augment evaluation authors, so that they can run more (and larger) evaluations more quickly. We encourage you to read our paper for more results/details: https://
anthropic.com/model-written-
evals.pdf
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Anthropic Hiring Research Engineers and Scientists for AI Evaluation
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We’re also actively hiring research engineers/scientists to develop evaluations and to find/fix flaws with LMs/RLHF. If you’re interested, we’d encourage you to apply!
Research engineer: https://
jobs.lever.co/Anthropic/436c
a148-6440-460f-b2a2-3334d9b142a5
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Research scientist: https://
jobs.lever.co/Anthropic/eb9e
6d83-626c-4f59-8a0e-fa7c413b2014
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Interactive Visualizations for Model-Written Dataset Evaluations Released
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To help readers understand our evaluations better, we created interactive visualizations showcasing the diversity of each of the model-written datasets: https://t.co/yc9oP9n9uV pic.twitter.com/R5IH7nTJw4
— Anthropic (@AnthropicAI) 19 décembre 2022To help readers understand our evaluations better, we created interactive visualizations showcasing the diversity of each of the model-written datasets: https://
evals.anthropic.com/model-written/ -
RLHF Training Shows Inverse Scaling Issues in Model Behavior
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We also find some of the first instances of inverse scaling for RL from Human Feedback (RLHF), where more RLHF training makes behavior worse. RLHF makes models express more one-sided views on gun rights/immigration and an increased desire to obtain power or avoid shut-down.
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Anthropic Creates Winogendered Dataset with 50x More Examples
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With more effort, we developed a series of LM generation/filtering stages to create a larger version of the popular Winogender bias dataset. Our “Winogenerated” evaluation contains 50x as many examples as the original while obeying complex grammatical constraints.
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Large Language Models More Sycophantic Than Small Ones
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Using these LM-written evals, we found many new instances of "inverse scaling," where larger LMs are worse than smaller ones. For example, larger LMs are more sycophantic, repeating back a user's views as their own in 75-98% of conversations.
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LM-written data verified by human evaluators for quality
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We verified LM-written data with human evaluators, who agreed with the data’s labels and rated the examples favorably on both diversity and relevance to the tested behavior. We’ve released our evaluations at
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Anthropic Explores Automating Language Model Evaluation
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We explored approaches with varying amounts of automation and human effort. In the simplest case, we generated thousands of yes-no questions for diverse behaviors just by instructing an LM (and filtering out bad examples with another LM). Random examples of LM-written evals: