# ChatPD: An LLM-driven Paper-Dataset Networking System

arXiv: https://arxiv.org/abs/2505.22349 · HTML: https://mlsanity.com/paper/2505.22349/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 27 of 27)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning](https://mlsanity.com/paper/1810.08272/) (2018)
- [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020)
- [VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping](https://mlsanity.com/paper/2304.07810/) (2023)
- [Dataset search: a survey](https://mlsanity.com/paper/1901.00735/) (2019)
- [ZeroER: Entity Resolution using Zero Labeled Examples](https://mlsanity.com/paper/1908.06049/) (2019)
- [Decomposed Meta-Learning for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2204.05751/) (2022)
- [HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction](https://mlsanity.com/paper/2410.09112/) (2024)
- [DMDD: A Large-Scale Dataset for Dataset Mentions Detection](https://mlsanity.com/paper/2305.11779/) (2023)
- [Lost or found? Discovering data needed for research](https://mlsanity.com/paper/1909.00464/) (2019)
- [Everything you always wanted to know about a dataset: studies in data summarisation](https://mlsanity.com/paper/1810.12423/) (2018)
- [Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2205.14228/) (2022)
- [Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2302.06397/) (2023)
- [Extracting Shopping Interest-Related Product Types from the Web](https://mlsanity.com/paper/2305.14549/) (2023)

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

- [Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator](https://mlsanity.com/paper/2507.11810/) (2025)
- [Multi-Disciplinary Dataset Discovery from Citation-Verified Literature Contexts](https://mlsanity.com/paper/2601.05099/) (2026)
- [DeepJSONEval: Benchmarking Complex Nested JSON Data Mining for Large Language Models](https://mlsanity.com/paper/2509.25922/) (2025)
- [SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Exploration](https://mlsanity.com/paper/2603.07502/) (2026)
- [An End-to-End Framework for Functionality-Embedded Provenance Graph Construction and Threat Interpretation](https://mlsanity.com/paper/2603.17100/) (2026)
- [Paper2Data: Large-Scale LLM Extraction and Metadata Structuring of Global Urban Data from Scientific Literature](https://mlsanity.com/paper/2604.16317/) (2026)
- [Large language model-assisted discovery of cohorts from scientific literature](https://mlsanity.com/paper/2608.15909/) (2026)

## Abstract (arXiv)

Scientific research heavily depends on suitable datasets for method validation, but existing academic platforms with dataset management like PapersWithCode suffer from inefficiencies in their manual workflow. To overcome this bottleneck, we present a system, called ChatPD, that utilizes Large Language Models (LLMs) to automate dataset information extraction from academic papers and construct a structured paper-dataset network. Our system consists of three key modules: textitpaper collection, textitdataset information extraction, and textitdataset entity resolution to construct paper-dataset networks. Specifically, we propose a textitGraph Completion and Inference strategy to map dataset descriptions to their corresponding entities. Through extensive experiments, we demonstrate that ChatPD not only outperforms the existing platform PapersWithCode in dataset usage extraction but also achieves about 90% precision and recall in entity resolution tasks. Moreover, we have deployed ChatPD to continuously extract which datasets are used in papers, and provide a dataset discovery service, such as task-specific dataset queries and similar dataset recommendations. We open source ChatPD and the current paper-dataset network on this [GitHub repository]https://github.com/ChatPD-web/ChatPD.