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..
Mixture of LoRA Experts
Authors: Xun Wu, Shaohan Huang, Furu Wei
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted in both Natural Language Processing (NLP) and Vision & Language (V&L) domains validate the effects of MOLE. |
| Researcher Affiliation | Collaboration | 1Microsoft Research Asia 2Tsinghua Univeristy |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor are there any structured code-like blocks describing a procedure. |
| Open Source Code | Yes | Our code are available at https://github.com/yushuiwx/Mo LE.git. |
| Open Datasets | Yes | We conducted extensive experiments across various tasks, including Translation, Natural Language Inference (NLI), Struct to Text, Closed-Book QA, and multiple subtasks within the Big-Bench Hard (BBH) (Ghazal et al., 2013) dataset. We trained a single Lo RA on a combined dataset comprising ANLI-R1 (Nie et al., 2019), ANLI-R2 (Nie et al., 2019), and QNLI (Rajpurkar et al., 2018) datasets, as depicted in Table 5. |
| Dataset Splits | No | The paper describes training parameters such as learning rate, batch size, and iterations, and mentions 'test' sets for evaluation, but it does not explicitly specify a 'validation set' or a dedicated 'validation split' with specific percentages or counts for data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions models and frameworks like 'Stable Diffusion V2.1' and 'Flan-T5', but it does not list any specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | During training MOLE, we process the image resolution to 512 × 512 and set learning rate as 1e-5. We use DDPM sampler (Ho et al., 2020) with 50 steps in each case and train 400 iterations for each required composition with batch size 2 and α as 0.5. |