mlsanityData to 7 Oct 2026

SymbolicGPT: A Generative Transformer Model for Symbolic Regression

arXiv 2106.14131 ↗First posted 27 Jun 2021cs.LG cs.CL cs.SCcited by 101

Not on the map. Why?

Abstract (arXiv)

Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. Due to the richness of the space of mathematical expressions, symbolic regression is generally a challenging problem. While conventional approaches based on genetic evolution algorithms have been used for decades, deep learning-based methods are relatively new and an active research area. In this work, we present SymbolicGPT, a novel transformer-based language model for symbolic regression. This model exploits the advantages of probabilistic language models like GPT, including strength in performance and flexibility. Through comprehensive experiments, we show that our model performs strongly compared to competing models with respect to the accuracy, running time, and data efficiency.

Key facts. Cited by 101 papers in the map (data through 7 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 27 Jun 2021.

Builds on

13 of 29 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  5. 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
  6. 2019Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradientscited by 294
  7. 2019Deep Learning for Symbolic Mathematicscited by 266
  8. 2021Neural Symbolic Regression that Scalescited by 185
  9. 2018Learning Equations for Extrapolation and Controlcited by 151
  10. 2016Extrapolation and learning equationscited by 106
  11. 2019Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discoverycited by 93
  12. 2020Probabilistic Grammars for Equation Discoverycited by 36
1 more
  1. 2020Learning Symbolic Expressions via Gumbel-Max Equation Learner Networkscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 14; 2023: 20; 2024: 24; 2025: 24; 2026: 18.

101 papers.

Sort:

By area:

  1. 2022End-to-end symbolic regression with transformerscited by 195
  2. 2022Interpretable Scientific Discovery with Symbolic Regression: A Reviewcited by 105
  3. 2024LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discoverycited by 82
  4. 2022Towards Learning Universal Hyperparameter Optimizers with Transformerscited by 81
  5. 2023Transformer-based Planning for Symbolic Regressioncited by 72
  6. 2023ODEFormer: Symbolic Regression of Dynamical Systems with Transformerscited by 58
  7. 2022SymFormer: End-to-end symbolic regression using transformer-based architecturecited by 56
  8. 2022Deep Symbolic Regression for Recurrent Sequencescited by 53
  9. 2022Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discoverycited by 44
  10. 2023Deep Generative Symbolic Regression with Monte-Carlo-Tree-Searchcited by 40
  11. 2023Generating Synergistic Formulaic Alpha Collections via Reinforcement Learningcited by 37
  12. 2024In-Context Symbolic Regression: Leveraging Large Language Models for Function Discoverycited by 29
38 more
  1. 2023Deep Generative Symbolic Regressioncited by 27
  2. 2024Large Language Model-Based Evolutionary Optimizer: Reasoning with elitismcited by 27
  3. 2023Predicting Ordinary Differential Equations with Transformerscited by 26
  4. 2024AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factorscited by 25
  5. 2024Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learningcited by 22
  6. 2025DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experiencecited by 20
  7. 2023Controllable Neural Symbolic Regressioncited by 19
  8. 2023Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systemscited by 18
  9. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
  10. 2021Pretrained Language Models are Symbolic Mathematics Solvers too!cited by 17
  11. 2023Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regressioncited by 14
  12. 2023A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Datacited by 14
  13. 2024MMSR: Symbolic Regression is a Multi-Modal Information Fusion Taskcited by 13
  14. 2024LLM4ED: Large Language Models for Automatic Equation Discoverycited by 13
  15. 2022SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learningcited by 12
  16. 2025Generative Discovery of Partial Differential Equations by Learning from Math Handbookscited by 12
  17. 2022GSR: A Generalized Symbolic Regression Approachcited by 11
  18. 2023Priors for symbolic regressioncited by 10
  19. 2025A Neural Symbolic Model for Space Physicscited by 10
  20. 2022Automated Learning of Interpretable Models with Quantified Uncertaintycited by 8
  21. 2023A Transformer Model for Symbolic Regression towards Scientific Discoverycited by 8
  22. 2023RSRM: Reinforcement Symbolic Regression Machinecited by 7
  23. 2024In Context Learning and Reasoning for Symbolic Regression with Large Language Modelscited by 7
  24. 2025Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Modelscited by 7
  25. 2022A PINN Approach to Symbolic Differential Operator Discovery with Sparse Datacited by 6
  26. 2023Symbolic Regression for PDEs using Pruned Differentiable Programscited by 6
  27. 2023GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equationscited by 6
  28. 2024SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compressioncited by 6
  29. 2025Improving Monte Carlo Tree Search for Symbolic Regressioncited by 6
  30. 2023MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimationcited by 5
  31. 2023Boolformer: Symbolic Regression of Logic Functions with Transformerscited by 5
  32. 2024A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programmingcited by 5
  33. 2025AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledgecited by 5
  34. 2026LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Searchcited by 5
  35. 2024PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regressioncited by 4
  36. 2024ChatSR: Multimodal Large Language Models for Scientific Formula Discoverycited by 4
  37. 2024Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradientscited by 4
  38. 2025Bridging the Domain Gap in Equation Distillation with Reinforcement Feedbackcited by 4

50 most cited of 101 All 101 (JSON)