Harnessing Large Language Models as Post-hoc Correctors
Zhiqiang Zhong Kuangyu Zhou Davide Mottin
As Machine Learning (ML) models grow in size and demand higher-quality
training data, the expenses associated with re-training and fine-tuning these
models are escalating rapidly. Inspired by recent impressive achievements of
Large Language Models (LLMs) in different fields, this paper delves into the
question: can LLMs efficiently improve an ML's performance at a minimal cost?
We show that, through our proposed training-free framework LlmCorr, an LLM can
work as a post-hoc corrector to propose corrections for the predictions of an
arbitrary ML model. In particular, we form a contextual knowledge database by
incorporating the dataset's label information and the ML model's predictions on
the validation dataset. Leveraging the in-context learning capability of LLMs,
we ask the LLM to summarise the instances in which the ML model makes mistakes
and the correlation between primary predictions and true labels. Following
this, the LLM can transfer its acquired knowledge to suggest corrections for
the ML model's predictions. Our experimental results on the challenging
molecular predictions show that LlmCorr improves the performance of a number of
models by up to 39%.