  Pretrained large language models (LLMs) are able to solve a wide variety of
tasks through transfer learning. Various explainability methods have been
developed to investigate their decision making process. TracIn (Pruthi et al.,
2020) is one such gradient-based method which explains model inferences based
on the influence of training examples. In this paper, we explore the use of
TracIn to improve model performance in the parameter-efficient tuning (PET)
setting. We develop conversational safety classifiers via the prompt-tuning PET
method and show how the unique characteristics of the PET regime enable TracIn
to identify the cause for certain misclassifications by LLMs. We develop a new
methodology for using gradient-based explainability techniques to improve model
performance, G-BAIR: gradient-based automated iterative recovery. We show that
G-BAIR can recover LLM performance on benchmarks after manually corrupting
training labels. This suggests that influence methods like TracIn can be used
to automatically perform data cleaning, and introduces the potential for
interactive debugging and relabeling for PET-based transfer learning methods.
