"Claude feels more conversational than ChatGPT," says a happy Poe user, while another says, "I find Claude to be more interactive and creative in its storytelling."
@anthropicai
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Claude disponible sur Poe : témoignages clients et retours utilisateurs
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Some customer stories: @Quora has offered Claude to users through Poe, their AI Chat app. “Users describe Claude’s answers as detailed and easily understood, and they like that exchanges feel like natural conversation,” says Autumn Besselman from Quora. https://
vimeo.com/manage/videos/
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Anthropic Launches Claude and Claude Instant AI Models
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We’re offering two versions of Claude today: Claude and Claude Instant. Claude is a state-of-the-art high-performance model, while Claude Instant is a lighter, less expensive, and much faster option. We plan to introduce even more updates in the coming weeks.
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Claude: Anthropic’s AI Assistant for Helpful, Honest, Harmless Conversations
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Claude is based on Anthropic’s research into training helpful, honest, and harmless AI systems. Accessible through chat and API, Claude is capable of a wide variety of conversational and text processing tasks while maintaining a high degree of reliability and predictability.
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Claude: Multifunctional Conversational Assistant in Natural Language
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You can chat with Claude, give it prompts to generate text, get Q&A responses and summaries, translate between languages, give it multi-step instructions and more using natural language.
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Claude: Less Harmful, More Conversational and Controllable
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Early customers report that Claude is much less likely to produce harmful outputs, easier to converse with, and more steerable – so you can get your desired output with less effort. Claude can also take direction on personality, tone and behavior.
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Large Language Models RLHF Ethics Natural Language Principles
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This work and CAI both observe the same basic phenomenon: if language models are sufficiently large and we add enough RLHF to make them helpful, we can more effectively get them to abide by high-level ethical principles expressed in natural language.
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RLHF and Prompting Techniques for Targeted Model Behavior
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This means that if we have a target behavior (e.g. non-discrimination) we may be able to nudge models to achieve that target using IF/CoT prompting if RLHF alone is not sufficient. But we must be careful to check whether RLHF + prompting causes the models to overshoot the target.
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Cautious Optimism on the Ethics of Language Models
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We believe our results are cause for cautious optimism regarding the ability to train language models to abide by ethical principles, echoing encouraging results we saw in our earlier related work on Constitutional AI (CAI).
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Language Models Show Demographic Bias Tradeoffs in Decision Making
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Prompting models to avoid making decisions based on race achieves demographic parity at steps 300 (CoT) and 600 (IF) but causes the model to start to discriminate against white students at higher steps. (Note that we do not claim LMs should be used for automated decision making!)