Unstructured and structured data: Can we have the best of both worlds with large language models?
Not on the map. Why?
Abstract (arXiv)
This paper presents an opinion on the potential of using large language models to query on both unstructured and structured data. It also outlines some research challenges related to the topic of building question-answering systems for both types of data.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 59 of its 83 references that are in the map. First posted on arXiv 25 Apr 2023.
Builds on
59 of 83 references.
References · 59
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2017Axiomatic Attribution for Deep Networkscited by 3,350
- 2013Visualizing and Understanding Convolutional Networkscited by 2,278
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2016Variational Graph Auto-Encoderscited by 1,373
- 2019Strategies for Pre-training Graph Neural Networkscited by 964
- 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
- 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
38 more
- 2019Graph Neural Networks for Social Recommendationcited by 711
- 2020Estimating Training Data Influence by Tracing Gradient Descentcited by 565
- 2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
- 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
- 2020The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memescited by 485
- 2020Parameterized Explainer for Graph Neural Networkcited by 423
- 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
- 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
- 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
- 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
- 2023Mathematical Capabilities of ChatGPTcited by 263
- 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
- 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 210
- 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
- 2022Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanismcited by 171
- 2019Explainability Techniques for Graph Convolutional Networkscited by 160
- 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 152
- 2020Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functionscited by 144
- 2020Deep Entity Matching with Pre-Trained Language Modelscited by 143
- 2022Retrieval-Augmented Multimodal Language Modelingcited by 125
- 2021Generative Causal Explanations for Graph Neural Networkscited by 124
- 2020Graph Information Bottleneck for Subgraph Recognitioncited by 119
- 2020Drug discovery with explainable artificial intelligencecited by 118
- 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
- 2022Diffusion Visual Counterfactual Explanationscited by 96
- 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
- 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
- 2022Evaluating Explainability for Graph Neural Networkscited by 85
- 2020Molecular graph generation with Graph Neural Networkscited by 79
- 2022Diffusion Models for Counterfactual Explanationscited by 79
- 2021A Review on Graph Neural Network Methods in Financial Applicationscited by 77
- 2022Global Explainability of GNNs via Logic Combination of Learned Conceptscited by 64
- 2022GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networkscited by 57
- 2022CLEAR: Generative Counterfactual Explanations on Graphscited by 48
- 2020Model extraction from counterfactual explanationscited by 46
- 2022Reinforced Causal Explainer for Graph Neural Networkscited by 34
- 2022GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networkscited by 34
- 2022OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networkscited by 33
50 most cited of 59 All 59 (JSON)
Built on by
Citing papers per year: 2026: 2.
2 papers.