With the increasing ability of large language models (LLMs), in-context learning (ICL) has evolved as a new paradigm for natural language processing (NLP), where instead of fine- tuning the parameters of an LLM specific to a downstream task with labeled examples,a small number of such examples is appended to a prompt instruction for controlling the decoder's generation process. ICL, thus, is conceptually similar to a non-parametric approach, such as k-NN,where the prediction for each instance essentially depends on the local topology, i.e., on a localised set of similar instances and their labels (called few-shot examples). This suggests that a test instance in ICL is analogous to a query in IR, and similar examples in ICL retrieved from a training set relate to a set of documents retrieved from a collection in IR. While standard unsupervised ranking models can be used to retrieve these few-shot examples from a training set, the effectiveness of the examples can potentially be improved by re-defining the notion of relevance specific to its utility for the downstream task, i.e., considering an example to be relevant if including it in the prompt instruction leads to a correct prediction. With this task-specific notion of relevance, it is possible to train a supervised ranking model (e.g., a bi-encoder or cross-encoder), which potentially learns to optimally select the few-shot examples. We believe that the recent advances in neural rankers can potentially find a use case for this task of optimally choosing examples for more effective downstream ICL predictions.

Data-driven (deep) learning methods has led to parameterised abstractions of the data, often leading to stereotype societal biases in their predictions, e.g., predicting more frequently that women are weaker than men, or that African Americans are more likely to commit crimes than Caucasians. Standard approaches of mitigating such stereotypical biases from deep neural models include modifying the training dataset (pre-processing), or adjusting the model parameters with a bias-specific objective (in-processing). In our work, we approach this bias mitigation from a different perspective - that of an active learning-based selection of a subset of data instances towards training a model optimised for both effectiveness and fairness. Specifically speaking, the imbalances in the attribute value priors can be alleviated by constructing a balanced subset of the data instances with two selection objectives - first, of improving the model confidence of the primary task itself (a standard practice in active learning), and the second, of taking into account the parity of the model predictions with respect to the sensitive attributes, such as gender and race etc. We demonstrate that our proposed selection function achieves better results in terms of both the primary task effectiveness and fairness. The results are further shown to improve when this active learning-based data selection is combined with an in-process method of multi-objective training.

In this paper, we propose an augmented Graph Convolutional Network (GCN) mechanism wherein additional information of local interaction patterns between a node with its neighbors (specifically, in the form of distribution of cosine similarity values of a pre-trained node vector with its neighbors) is used to enrich a node's representation prior to training a GCN. This provides additional information about the structural properties of a node, which the standard convolution operation in a GCN can then leverage for obtaining potentially improved effectiveness in a down-stream task. Our experiments demonstrate that adding these node interaction patterns (NIPs) along with an additional noise-contrastive pairwise document similarity objective within a GCN improves the linked document classification task.