VER PARA CREER Acabo de probar la herramienta y efectivamente me ha transcrito 25 minutos de audio en ~20 segundos donde parte del tiempo se ha ido en la subida del archivo. ¿QUÉ MAGIA NEGRA ES ESTA? Mirad en el lateral el tamaño de la barra de scroll…
GENERATIVE AI
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ChatGPT Explanation: Impressive But Not Revolutionary
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@stephen_wolfram gives a necessarily long but very good explanation of what and how chatGPT works. My view: calm down people. There are important things there but not the shiny "world has changed for ever" techno-salvation that many people pining for.
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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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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!)
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RLHF Training Reduces but Doesn’t Eliminate Racial Discrimination in Admissions
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Finally, we develop a benchmark testing for racial discrimination in LM decision-making in student course admissions. In our control condition (blue) we find more RLHF training produces model outputs that approach demographic parity but still discriminates against Black students.
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Steering AI Models Toward Different Goals Through Directed Requests
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We have no position on which of these two goals is better or more desirable—it likely depends on the task and the context—but we do find we can easily steer models towards distinct goals by simply asking for different kinds of behavior.
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Reducing Bias in BBQ with Simple Prompts
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The prompt that reduces bias in BBQ by 43% is: "Please ensure that your answer is unbiased and does not rely on stereotyping." It's that simple! Augmenting the prompt with Chain-of-thought reasoning (CoT) reduces bias by 84%. Example prompts:
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Steering Language Models Away From Gender Stereotypes in Occupations
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We look at the Winogender benchmark and show we can steer larger models towards two different goals: to output pronouns that are correlated with occupational gender statistics from the U.S. Bureau of Labor Statistics (red) or to move away from using stereotypical pronouns (green)
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Prompting Techniques Reduce Harmful Biases in Large Language Models
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Language models (LMs) exhibit harmful biases that can get worse with size. Reinforcement learning from human feedback (RLHF) helps, but not always enough. We show that simple prompting approaches can help LMs trained with RLHF produce less harmful outputs. https://
arxiv.org/abs/2302.07459