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..
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
Authors: Ethan Perez, Douwe Kiela, Kyunghyun Cho
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce a method to determine if a certain capability helps to achieve an accurate model of given data... we showcase its applicability on a wide variety of settings in NLP, ranging from evaluating the utility of generating subquestions before answering a question, to analyzing the value of rationales and explanations, to investigating the importance of different parts of speech, and uncovering dataset gender bias. |
| Researcher Affiliation | Collaboration | 1New York University 2Facebook AI Research 3CIFAR Fellow in Learning in Machines & Brains. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block found in the paper. |
| Open Source Code | Yes | Code at https://github.com/ethanjperez/rda along with a script to conduct RDA on your own dataset. |
| Open Datasets | Yes | To this end, we use CLEVR (Johnson et al., 2017), an image-based question-answering (QA) dataset. |
| Dataset Splits | Yes | To train a model on the ๏ฌrst s blocks, we split the available examples into train (90%) and dev (10%) sets, choosing hyperparameters and early stopping epoch using dev loss (codelength). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are explicitly mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like Hugging Face Transformers, PyTorch Lightning, and FastText, but does not provide specific version numbers for these or other key software components. |
| Experiment Setup | Yes | We use S = 9 blocks where t0 = 0 and t1 = 64 < < t S = N such that ts+1 is constant (log-uniform ts spacing). To train a model on the ๏ฌrst s blocks, we split the available examples into train (90%) and dev (10%) sets, choosing hyperparameters and early stopping epoch using dev loss (codelength). We otherwise follow each model s training strategy and hyperparameter ranges as suggested by its original paper. We then evaluate the codelength of the (s + 1)-th block. |