Evidence-grounded Interpretable Local Citation Recommendation System: ILCiteR

Core Concepts

ILCiteR introduces evidence-grounded local citation recommendation for interpretability, leveraging distant supervision and pre-trained models.

Abstract

ILCiteR proposes a novel approach to local citation recommendation by focusing on interpretability through evidence spans. It utilizes a distantly-supervised evidence retrieval system and pre-trained Transformer-based Language Models for recommendations without explicit model training. The system re-ranks evidence spans based on query similarity and ranks associated papers for each span. Key contributions include a new dataset, conditional neural rank ensembling, and improved downstream paper recommendation performance.

Stats

Over 200,000 unique evidence spans in the dataset. No explicit model training required for ILCiteR. Performance improvements over lexical and semantic similarity based methods.

Quotes

Key Insights Distilled From

ILCiteR

by Sayar Ghosh ... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08737.pdf

Deeper Inquiries

How can the interpretability of local citation recommendations impact research practices?

Interpretability in local citation recommendations can significantly impact research practices by providing researchers with insights into why a particular paper is being recommended for a specific query. This transparency allows researchers to understand the rationale behind the recommendation, leading to more informed decision-making when citing papers. Researchers can gain valuable context on how their work aligns with existing literature, helping them identify relevant studies and build upon previous research effectively. Additionally, interpretability fosters trust in the recommendation system, enhancing its credibility and encouraging researchers to explore a wider range of sources.

What are the potential limitations of relying solely on distant supervision for citation recommendations?

While distant supervision offers benefits such as scalability and reduced manual effort in creating training data for citation recommendation systems like ILCiteR, it also comes with certain limitations. One key limitation is the quality of the evidence database used for distant supervision. The accuracy and relevance of retrieved evidence spans heavily influence the performance of the recommendation system. Inaccurate or irrelevant evidence spans can lead to suboptimal recommendations, impacting the overall effectiveness of the system. Another limitation is related to bias in data collection. Distant supervision relies on existing datasets or databases which may already contain inherent biases present in academic literature or citation patterns. This could result in biased recommendations that perpetuate existing trends rather than promoting diversity and novelty in citations. Furthermore, relying solely on distant supervision may limit adaptability to new trends or emerging topics where there might be limited historical data available for training purposes. The system's ability to recommend papers outside established norms or conventional wisdom may be constrained by this reliance on past information.

How might incorporating additional contextual information improve the accuracy of paper recommendations in systems like ILCiteR?

Incorporating additional contextual information can enhance the accuracy of paper recommendations in systems like ILCiteR by providing a more comprehensive understanding of queries and their relationships with cited papers. Topic Modeling: By considering topic modeling techniques, such as Latent Dirichlet Allocation (LDA), systems like ILCiteR can better capture thematic similarities between queries and recommended papers. Named Entity Recognition: Integrating Named Entity Recognition (NER) capabilities enables identification and extraction of key entities from both queries and documents, facilitating more precise matching based on entity mentions. Temporal Information: Including temporal information about publications allows for prioritizing recent works over older ones when making recommendations. Semantic Similarity: Leveraging semantic similarity measures beyond lexical overlap enhances understanding at a deeper level than surface-level keyword matching. By integrating these contextual cues into recommendation algorithms within ILCiteR, it becomes possible to generate more accurate suggestions tailored specifically to each query's unique characteristics while improving overall relevance and informativeness levels across different domains within scientific literature retrieval tasks.