7 papers with code • 5 benchmarks • 2 datasets
These leaderboards are used to track progress in Citation Prediction
Most implemented papers
SPECTER: Document-level Representation Learning using Citation-informed Transformers
We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph.
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval.
Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks
For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent.
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics.
SGPT: GPT Sentence Embeddings for Semantic Search
To this end, we propose SGPT to use decoders for sentence embeddings and semantic search via prompting or fine-tuning.
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
This has made distilled and dense models, due to latency constraints, the go-to choice for deployment in real-world retrieval applications.
Galactica: A Large Language Model for Science
We believe these results demonstrate the potential for language models as a new interface for science.