Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Language Modeling with Gated Convolutional Networks
Authors: Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,822 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,262 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report results on two public large-scale language modeling datasets. First, the Google Billion Word dataset (Chelba et al., 2013)... Second, Wiki Text-103... (Merity et al., 2016). We compare the different gating schemes experimentally in Section 5.2 |
| Researcher Affiliation | Industry | 1Facebook AI Research. Correspondence to: Yann N. Dauphin <EMAIL>. |
| Pseudocode | No | The paper presents mathematical equations and describes the architecture verbally but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We report results on two public large-scale language modeling datasets. First, the Google Billion Word dataset (Chelba et al., 2013)... Second, Wiki Text-103... (Merity et al., 2016). |
| Dataset Splits | No | The paper states 'We found good hyper-parameter configurations by crossvalidating with random search on a validation set.' and mentions evaluating 'on the standard held out test portion of each dataset'. However, it does not provide specific percentages or counts for the validation set, nor detailed methodology for its split. |
| Hardware Specification | Yes | We implement our models in Torch (Collobert et al., 2011) and train on Tesla M40 GPUs. The majority of our models are trained on single GPU... We trained larger models with an 8-GPU setup... |
| Software Dependencies | No | The paper states 'We implement our models in Torch (Collobert et al., 2011)'. While the citation points to 'Torch7', the text itself only mentions 'Torch' without an explicit version number for the software used in their implementation. |
| Experiment Setup | Yes | In terms of optimization, we initialize the layers of the model with the Kaiming initialization (He et al., 2015b), with the learning rate sampled uniformly in the interval [1., 2.], the momentum set to 0.99, and clipping set to 0.1. |