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..
Hash Layers For Large Sparse Models
Authors: Stephen Roller, Sainbayar Sukhbaatar, arthur szlam, Jason Weston
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the training of sparse layers... We show that this procedure either outperforms or is competitive with learning-to-route mixture-of-expert methods... We study the performance of different hashing techniques... We show our approach works well both on large language modeling and dialogue tasks, and on downstream fine-tuning tasks. |
| Researcher Affiliation | Industry | Stephen Roller Sainbayar Sukhbaatar Arthur Szlam Jason Weston Facebook AI Research |
| Pseudocode | No | The paper describes methods in text and uses mathematical formulas but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a concrete access link (e.g., GitHub URL) or an explicit statement confirming the release of source code for the methodology described in this paper. |
| Open Datasets | Yes | Pushshift.io Reddit We use a variant of Reddit discussions... made available on pushshift.io [33]... RoBERTa+cc100en Data We use the same data used to train BASE [10]... Wikitext-103 Wikitext-103 is a smaller language modeling benchmark [37]... |
| Dataset Splits | Yes | The load balancing for Switch is optimized on the validation set.Model Configuration Params Valid PPL Test PPL |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or detailed computer specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'ParlAI' platform and 'Fairseq' codebase, but it does not specify concrete version numbers for these or other software dependencies required for replication. |
| Experiment Setup | Yes | The majority of our experiments are carried out in Parl AI1 platform using an encoder-decoder Transformer framework... We refer to the one with 11 layers and embedding size of d = 1024 and FFN hidden layer size of D = 4096 as our Baseline Transformer... All experiments are run for 100k updates; a table of hyperparameters is provided in subsection B.1. |