Researchers at Google and University of Illinois at Urbana-Champaign (UIUC) have published a technique called Language Model Self-Improved (LMSI), which fine-tunes a large language model (LLM) on a dataset generated by that same model. Using LMSI, the researchers improved the performance of the LLM on six benchmarks and set new state-of-the-art accuracy records on four of them. The team began with a pre-trained 540B parameter PaLM model. The model was given as input questions from an unlabeled training dataset, along with chain-of-thought prompts. The model generated answers for these questions, which were then used along with the inputs as a fine-tuning training dataset. The fine-tuned model was then evaluated on…