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Fine-tuning language models to find agreement among humans with diverse preferences
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user.
Language models show human-like content effects on reasoning tasks
We evaluate state of the art large language models, as well as humans, and find that the language models reflect many of the same patterns observed in humans across these tasks $\unicode{x2014}$ like humans, models answer more accurately when the semantic content of a task supports the logical inferences.
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