  Existing Machine Learning approaches for local citation recommendation
directly map or translate a query, which is typically a claim or an entity
mention, to citation-worthy research papers. Within such a formulation, it is
challenging to pinpoint why one should cite a specific research paper for a
particular query, leading to limited recommendation interpretability. To
alleviate this, we introduce the evidence-grounded local citation
recommendation task, where the target latent space comprises evidence spans for
recommending specific papers. Using a distantly-supervised evidence retrieval
and multi-step re-ranking framework, our proposed system, ILCiteR, recommends
papers to cite for a query grounded on similar evidence spans extracted from
the existing research literature. Unlike past formulations that simply output
recommendations, ILCiteR retrieves ranked lists of evidence span and
recommended paper pairs. Secondly, previously proposed neural models for
citation recommendation require expensive training on massive labeled data,
ideally after every significant update to the pool of candidate papers. In
contrast, ILCiteR relies solely on distant supervision from a dynamic evidence
database and pre-trained Transformer-based Language Models without any model
training. We contribute a novel dataset for the evidence-grounded local
citation recommendation task and demonstrate the efficacy of our proposed
conditional neural rank-ensembling approach for re-ranking evidence spans.
