Pareto Optimal Learning Improves Large Language Model Accuracy
Researchers propose Pareto Optimal Self-Supervision (POSS) to automatically correct large language model errors & biases by leveraging output diversity & uncertainty.
This is a Plain English Papers summary of a research paper called Pareto Optimal Learning for Estimating Large Language Model Errors. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter. Overview This paper proposes a novel approach for automatically calibrating and correcting errors in large language models (LLMs) through a technique called Pareto Optimal Self-Supervision (POSS). The key idea is to leverage the intrinsic uncertainty and diversity of LLM outputs to identify and correct systematic errors and biases. The auth...