Leveraging LLMs for Solomonoff-Inspired Hypothesis Ranking in Uncertain Prediction

This research explores a novel application of Large Language Models (LLMs) to address prediction under uncertainty, drawing inspiration from Solomonoff's theory of inductive inference. The work's impact depends significantly on the empirical validation of the proposed method's predictive accuracy and efficiency.

• •Applies LLMs to hypothesis ranking for prediction under uncertainty.
• •Inspired by Solomonoff's theory.
• •Focuses on improved prediction accuracy and efficiency.