# PST-Bench: Tracing and Benchmarking the Source of Publications

arXiv: https://arxiv.org/abs/2402.16009 · HTML: https://mlsanity.com/paper/2402.16009/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [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)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization](https://mlsanity.com/paper/1906.11156/) (2019)
- [CogDL: A Comprehensive Library for Graph Deep Learning](https://mlsanity.com/paper/2103.00959/) (2021)
- [Incidental or influential? - Challenges in automatically detecting citation importance using publication full texts](https://mlsanity.com/paper/1707.04207/) (2017)
- [IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications](https://mlsanity.com/paper/2209.13243/) (2022)

## Built on by (most cited first, 7 of 7)

- [Sci-Reasoning: A Dataset Decoding AI Innovation Patterns](https://mlsanity.com/paper/2601.04577/) (2026)
- [In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis](https://mlsanity.com/paper/2505.14838/) (2025)
- [Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2505.14471/) (2025)
- [The Solution for The PST-KDD-2024 OAG-Challenge](https://mlsanity.com/paper/2407.12827/) (2024)
- [Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing](https://mlsanity.com/paper/2407.17722/) (2024)
- [LLM-Powered Ensemble Learning for Paper Source Tracing: A GPU-Free Approach](https://mlsanity.com/paper/2409.09383/) (2024)
- [Crystal: Characterizing Relative Impact of Scholarly Publications](https://mlsanity.com/paper/2603.26791/) (2026)

## Abstract (arXiv)

Tracing the source of research papers is a fundamental yet challenging task for researchers. The billion-scale citation relations between papers hinder researchers from understanding the evolution of science efficiently. To date, there is still a lack of an accurate and scalable dataset constructed by professional researchers to identify the direct source of their studied papers, based on which automatic algorithms can be developed to expand the evolutionary knowledge of science. In this paper, we study the problem of paper source tracing (PST) and construct a high-quality and ever-increasing dataset PST-Bench in computer science. Based on PST-Bench, we reveal several intriguing discoveries, such as the differing evolution patterns across various topics. An exploration of various methods underscores the hardness of PST-Bench, pinpointing potential directions on this topic. The dataset and codes have been available at https://github.com/THUDM/paper-source-trace.