diagNNose: A Library for Neural Activation Analysis
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Abstract (arXiv)
In this paper we introduce diagNNose, an open source library for analysing the activations of deep neural networks. diagNNose contains a wide array of interpretability techniques that provide fundamental insights into the inner workings of neural networks. We demonstrate the functionality of diagNNose with a case study on subject-verb agreement within language models. diagNNose is available at https://github.com/i-machine-think/diagnnose.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Nov 2020.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 1; 2024: 1.
7 papers.
- 2022minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Modelscited by 58
- 2021Structural Persistence in Language Models: Priming as a Window into Abstract Language Representationscited by 44
- 2021Language Models Use Monotonicity to Assess NPI Licensingcited by 30
- 2024Do Language Models Exhibit Human-like Structural Priming Effects?cited by 21
- 2023NeuroX Library for Neuron Analysis of Deep NLP Modelscited by 11
- 2021Language Modelling as a Multi-Task Problemcited by 10
- 2021Attention vs non-attention for a Shapley-based explanation methodcited by 3
- 2024Do Language Models Exhibit Human-like Structural Priming Effects?7 Jun 2024
- 2023NeuroX Library for Neuron Analysis of Deep NLP Models26 May 2023
- 2022minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models24 Mar 2022
- 2021Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations30 Sep 2021
- 2021Language Models Use Monotonicity to Assess NPI Licensing28 May 2021
- 2021Attention vs non-attention for a Shapley-based explanation method26 Apr 2021
- 2021Language Modelling as a Multi-Task Problem27 Jan 2021