'One size doesn't fit all': Learning how many Examples to use for
  In-Context Learning for Improved Text Classification
Manish Chandra Debasis Ganguly Yiwen Li Iadh Ounis
Predictive models in natural language processing (NLP) have evolved from
training models from scratch to fine-tuning pre-trained models with labelled
data. An extreme form of this fine-tuning involves in-context learning (ICL),
where the output of a pre-trained generative model (frozen decoder parameters)
is controlled only with variations in the input strings (called instructions or
prompts). An important component of ICL is the use of a small number of
labelled data instances as examples in the prompt. While existing work uses a
static number of examples during inference for each data instance, in this
paper we propose a novel methodology of dynamically adapting the number of
examples as per the data. This is analogous to the use of a variable-sized
neighborhood in k-nearest neighbors (k-NN) classifier. In our proposed workflow
of adaptive ICL (AICL), the number of demonstrations to employ during the
inference on a particular data instance is predicted by the Softmax posteriors
of a classifier. The parameters of this classifier are fitted on the optimal
number of examples in ICL required to correctly infer the label of each
instance in the training set with the hypothesis that a test instance that is
similar to a training instance should use the same (or a closely matching)
number of few-shot examples. Our experiments show that our AICL method results
in improvement in text classification task on several standard datasets.