Direct Preference Optimization
Literally upweight the preferred response while downweight the unpreferred response, which is a really simple mechanism.
It can learn human preferences without RL, using significantly less memory compared to the PPO architecture in RLHF.
The model directly incorporates user's pairwise preferences into training using a set of preference pairs and logits.
{'prompt': '<|im_start|>system\nYou are an AI assistant. You will be given a task. You must generate a detailed and long answer.<|im_end|>\n<|im_start|>user\nGenerate an approximately fifteen-word sentence that describes all this data: Midsummer House eatType restaurant; Midsummer House food Chinese; Midsummer House priceRange moderate; Midsummer House customer rating 3 out of 5; Midsummer House near All Bar One<|im_end|>\n<|im_start|>assistant\n', 'chosen': 'Midsummer House is a moderately priced Chinese restaurant with a 3/5 customer rating, located near All Bar One.<|im_end|>\n', 'rejected': ' Sure! Here\'s a sentence that describes all the data you provided:\n\n"Midsummer House is a moderately priced Chinese restaurant with a customer rating of 3 out of 5, located near All Bar One, offering a variety of delicious dishes."<|im_end|>\n'}