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PIT is implicitly trained with the improvement goal of better aligning with human preferences. Recent years have seen remarkable advances in natural language processing capabilities thanks to the rise of like GPT-3, PaLM, and Anthropic's Claude. These foundation models can generate human-like text across a diverse range of applications, from conversational assistants to summarizing complex information.
Technical Details on the PIT Approach At a high level, the an LLM policy to maximize the expected quality of generated responses. PIT reformulates this to maximize the gap in quality between the original response and an improved response conditioned on having the original as a reference point. standard RLHF objective optimizes The key is the training data that indicates human preferences between good and bad responses already provides implicit guidance on the dimension of improvement.
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