mlsanityData to 7 Oct 2026

py-irt: A Scalable Item Response Theory Library for Python

arXiv 2203.01282 ↗First posted 2 Mar 2022cs.CLcited by 19

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Abstract (arXiv)

py-irt is a Python library for fitting Bayesian Item Response Theory (IRT) models. py-irt estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as ideal-point models. py-irt is built on top of the Pyro and PyTorch frameworks and uses GPU-accelerated training to scale to large data sets. Code, documentation, and examples can be found at https://github.com/nd-ball/py-irt. py-irt can be installed from the GitHub page or the Python Package Index (PyPI).

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 10 of its 50 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

10 of 50 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  3. 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
  4. 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
  5. 2021Deep Learning Through the Lens of Example Difficultycited by 139
  6. 2021Comparing Test Sets with Item Response Theorycited by 55
  7. 2019Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowdscited by 40
  8. 2020Variational Item Response Theory: Fast, Accurate, and Expressivecited by 29
  9. 2020Dynamic Data Selection for Curriculum Learning via Ability Estimationcited by 21
  10. 2020A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysiscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 6; 2026: 8.

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  1. 2024tinyBenchmarks: evaluating LLMs with fewer examplescited by 295
  2. 2024TimeSeriesExam: A time series understanding examcited by 47
  3. 2025How to Select Datapoints for Efficient Human Evaluation of NLG Models?cited by 23
  4. 2025MERGE3: Efficient Evolutionary Merging on Consumer-grade GPUscited by 9
  5. 2025How Reliable is Language Model Micro-Benchmarking?cited by 8
  6. 2024A Psychology-based Unified Dynamic Framework for Curriculum Learningcited by 6
  7. 2025Investigating the Zone of Proximal Development of Language Models for In-Context Learningcited by 6
  8. 2026BRIDGE: Predicting Human Task Completion Time From Model Performancecited by 6
  9. 2025Revisiting Generalization Across Difficulty Levels: It's Not So Easycited by 5
  10. 2026Can We Trust Item Response Theory for AI Evaluation?cited by 3
  11. 2023Computational modeling of semantic changecited by 2
  12. 2025LiveRAG: A diverse Q&A dataset with varying difficulty level for RAG evaluationcited by 2
7 more
  1. 2026Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Resultscited by 2
  2. 2024Controlling Cloze-test Question Item Difficulty with PLM-based Surrogate Models for IRT Assessmentcited by 1
  3. 2026The Scaling Law of Evaluation Failure: Why Simple Averaging Collapses Under Data Sparsity and Item Difficulty Gaps, and How Item Response Theory Recovers Ground Truth Across Domainscited by 1
  4. 2026Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignmentcited by 0
  5. 2026An Interpretable and Scalable Framework for Evaluating Large Language Modelscited by 0
  6. 2026Efficient Safety Benchmarking via Item Response Theorycited by 0
  7. 2026What Makes Something Hard(er)? Explaining Question Difficulty in Natural Languagecited by 0