A team of AI developers is refining a language model using a dataset of human preferences. Each data point consists of a prompt, a 'chosen' response, and a 'rejected' response. Instead of first training a separate model to score how good a response is and then using that score to guide the language model, they directly adjust the main language model's parameters to increase the probability of generating 'chosen' responses over 'rejected' ones. What is a key advantage of this direct adjustment method?
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Ch.4 Alignment - Foundations of Large Language Models
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A team of AI developers is refining a language model using a dataset of human preferences. Each data point consists of a prompt, a 'chosen' response, and a 'rejected' response. Instead of first training a separate model to score how good a response is and then using that score to guide the language model, they directly adjust the main language model's parameters to increase the probability of generating 'chosen' responses over 'rejected' ones. What is a key advantage of this direct adjustment method?
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