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..
Emergent Communication of Generalizations
Authors: Jesse Mu, Noah Goodman
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We examine the languages developed for our proposed communication games over two datasets: first, an artificial shape dataset which allows us to evaluate communication over cleanly defined logical concepts; second, a dataset of birds to test agents ability to learn concepts from realistic visual input. |
| Researcher Affiliation | Academia | Jesse Mu Stanford University EMAIL Noah Goodman Stanford University EMAIL |
| Pseudocode | No | The paper describes the models and training procedure textually but does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | For full model and training details and a link to code, see Appendix A. |
| Open Datasets | Yes | We use the Shape World visual reasoning dataset [18] (Figure 2, left). We next use the Caltech-UCSD Birds dataset [40] which contains 200 classes of birds with 40 60 images (Figure 2, right). |
| Dataset Splits | No | The paper specifies test splits (e.g., '20% held out for testing', '100 classes at train and 50 at test') but does not explicitly mention a separate validation set used during training. |
| Hardware Specification | Yes | All models were trained on a single NVIDIA 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions using 'PyTorch' and the 'Transformers library' but does not specify their version numbers. It also mentions the Adam optimizer [15], which is an algorithm. |
| Experiment Setup | Yes | We use the Adam optimizer [15] with learning rate 10−4, and a batch size of 64. |