  Recently, influence functions present an apparatus for achieving
explainability for deep neural models by quantifying the perturbation of
individual train instances that might impact a test prediction. Our objectives
in this paper are twofold. First we incorporate influence functions as a
feedback into the model to improve its performance. Second, in a dataset
extension exercise, using influence functions to automatically identify data
points that have been initially `silver' annotated by some existing method and
need to be cross-checked (and corrected) by annotators to improve the model
performance. To meet these objectives, in this paper, we introduce InfFeed,
which uses influence functions to compute the influential instances for a
target instance. Toward the first objective, we adjust the label of the target
instance based on its influencer(s) label. In doing this, InfFeed outperforms
the state-of-the-art baselines (including LLMs) by a maximum macro F1-score
margin of almost 4% for hate speech classification, 3.5% for stance
classification, and 3% for irony and 2% for sarcasm detection. Toward the
second objective we show that manually re-annotating only those silver
annotated data points in the extension set that have a negative influence can
immensely improve the model performance bringing it very close to the scenario
where all the data points in the extension set have gold labels. This allows
for huge reduction of the number of data points that need to be manually
annotated since out of the silver annotated extension dataset, the influence
function scheme picks up ~1/1000 points that need manual correction.
