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Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas

Generally Intelligent

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Preferences Over Defaults Lead to Better Outcomes

Large language models typically perform better as they scale; however, specific tasks reveal an interesting dynamic where these models can underperform based on their inherent biases. A key finding is that overriding a model's default behavior—especially when tasked to avoid typical endings—can align performance more closely with human preferences. This highlights the importance of adaptability in AI, indicating that effectiveness may hinge on the model's capacity to shift from its programmed inclinations to better cater to user requests, suggesting that understanding this adaptability could lead to more valuable human-centered benchmarks.

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