# ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation

arXiv: https://arxiv.org/abs/2403.08737 · HTML: https://mlsanity.com/paper/2403.08737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Weakly-Supervised Hierarchical Text Classification](https://mlsanity.com/paper/1812.11270/) (2018)
- [Citation Recommendation: Approaches and Datasets](https://mlsanity.com/paper/2002.06961/) (2020)
- [A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks](https://mlsanity.com/paper/1903.06464/) (2019)
- [Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking](https://mlsanity.com/paper/2112.01206/) (2021)
- [hyperdoc2vec: Distributed Representations of Hypertext Documents](https://mlsanity.com/paper/1805.03793/) (2018)
- [HybridCite: A Hybrid Model for Context-Aware Citation Recommendation](https://mlsanity.com/paper/2002.06406/) (2020)

## Built on by (most cited first, 3 of 3)

- [DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review](https://mlsanity.com/paper/2604.09590/) (2026)
- [LitBench: A Graph-Centric Large Language Model Benchmarking Tool For Literature Tasks](https://mlsanity.com/paper/2603.00051/) (2026)
- [ATTRICITE: Training an Open 4B Model for Citation Recovery toward Faithful Attribution](https://mlsanity.com/paper/2609.14248/) (2026)

## Abstract (arXiv)

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.