Large Language Models Learn To Self-Improve From Human Preferences
Large language models can now improve themselves without explicit human guidance thanks to the "ImPlicit Self-ImprovemenT" (PIT) framework, which learns improvement goals from human preference data.
This is a Plain English Papers summary of a research paper called Language Models Learn to Self-Improve Implicitly from Human Preferences. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview Large language models (LLMs) have made remarkable progress in open-ended text generation tasks. However, there is always room for improvement in the quality of model responses. Researchers have proposed various approaches to enhance the performance of LLMs, including enabling them to self-improve their response quality. Prompting-based methods have...