Team LA at SCIDOCA shared task 2025: Citation Discovery via relation-based zero-shot retrieval
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Abstract (arXiv)
The Citation Discovery Shared Task focuses on predicting the correct citation from a given candidate pool for a given paragraph. The main challenges stem from the length of the abstract paragraphs and the high similarity among candidate abstracts, making it difficult to determine the exact paper to cite. To address this, we develop a system that first retrieves the top-k most similar abstracts based on extracted relational features from the given paragraph. From this subset, we leverage a Large Language Model (LLM) to accurately identify the most relevant citation. We evaluate our framework on the training dataset provided by the SCIDOCA 2025 organizers, demonstrating its effectiveness in citation prediction.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 23 Jun 2025.
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References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
- 2016Enriching Word Vectors with Subword Informationcited by 1,583
- 2019Passage Re-ranking with BERTcited by 645
- 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 463
- 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
- 2019Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphscited by 71
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