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..
Identifying and Controlling Important Neurons in Neural Machine Translation
Authors: Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, James Glass
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. |
| Researcher Affiliation | Collaboration | 1MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA 2Qatar Computing Research Institute, HBKU Research Complex, Doha 5825, Qatar |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available as part of the Neuro X toolkit (Dalvi et al., 2019b).7https://github.com/fdalvi/Neuro X |
| Open Datasets | Yes | We use the United Nations (UN) parallel corpus (Ziemski et al., 2016) for all experiments. |
| Dataset Splits | No | The paper mentions training models on "different parts of the training set" and evaluating on "the official test set", but does not explicitly detail a separate validation set split. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Spacy for linguistic annotations and refers to a character convolutional neural network (char CNN) and LSTM encoder-decoder models, but it does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We train 500 dimensional 2-layer LSTM encoder-decoder models with attention (Bahdanau et al., 2014). In order to study both word and sub-word properties, we use a word representation based on a character convolutional neural network (char CNN) as input to both encoder and decoder. |