Direct Preference Optimization (DPO)

2 Posts

More Factual LLMs: FactTune, a method to fine-tune LLMs for factual accuracy without human feedback

Large language models sometimes generate false statements. New work makes them more likely to produce factual output.

Human Feedback Without Reinforcement Learning: Direct Preference Optimization (DPO) fine-tunes pretrained large language models on human preferences without the cumbersome step of reinforcement learning.

Reinforcement learning from human feedback (RLHF) is widely used to fine-tune pretrained models to deliver outputs that align with human preferences. New work aligns pretrained models without the cumbersome step of reinforcement learning.

Direct Preference Optimization (DPO)

More Factual LLMs: FactTune, a method to fine-tune LLMs for factual accuracy without human feedback

Human Feedback Without Reinforcement Learning: Direct Preference Optimization (DPO) fine-tunes pretrained large language models on human preferences without the cumbersome step of reinforcement learning.

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