Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Robustness of Text Vectorizers
Authors: Rémi Catellier, Samuel Vaiter, Damien Garreau
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These findings are exemplified through a series of numerical examples. (Abstract); 4.2. Experimental validation; 5.3. Experimental validation |
| Researcher Affiliation | Academia | 1Universit e Cˆote d Azur, CNRS, LJAD, France 2Inria, France 3CNRS, France. |
| Pseudocode | No | The paper presents mathematical proofs and formalisms but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for all experiments of the paper is available at https://github.com/dgarreau/vectorizer-robustness. |
| Open Datasets | Yes | We considered movie reviews from the IMDB dataset as documents and the TF-IDF implementation from scikit-learn with L2 normalization. (Section 4.2); We considered again movie reviews from the IMDB dataset. (Section 5.3) |
| Dataset Splits | No | The paper mentions using a "subset of the IMDB dataset (10^3 reviews)" for training and describes how they perturb documents for experiments ("replaced 5 words", "increased the number of replaced words"), but it does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "scikit-learn" and "gensim" but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We chose d = 50 as dimension of the embedding. We took ν = 5 as context size parameter. (Section 5.3) |