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..
Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE
Authors: Zeren Chen, Ziqin Wang, Zhen Wang, Huayang Liu, Zhenfei Yin, Si Liu, Lu Sheng, Wanli Ouyang, Jing Shao
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results (about 20% improvement) have shown the effectiveness and versatility of our design in various 2D and 3D downstream tasks. |
| Researcher Affiliation | Collaboration | 1 Shanghai AI Laboratory, 2 School of Software, Beihang University, 3 Institute of Artificial Intelligence, Beihang University, 4 University of Sydney |
| Pseudocode | No | The paper describes methods in prose and diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and datasets are available at https://openlamm.github.io/paper list/Octavius. |
| Open Datasets | Yes | Code and datasets are available at https://openlamm.github.io/paper list/Octavius. |
| Dataset Splits | No | The paper uses several well-known datasets but does not explicitly provide the training/validation/test splits for all of them needed for reproduction. For instance, it mentions 'fine-tune Octavius' and 'zero-shot evaluation' on various datasets but does not detail the splits used for training or validation across all datasets. |
| Hardware Specification | Yes | All experiments are conducted on 4 NVIDIA A100 80GB GPUs. |
| Software Dependencies | No | The paper mentions software components like Vicuna-13B, SentencePiece, and Adam optimizer, but does not provide specific version numbers for the underlying software libraries (e.g., PyTorch, TensorFlow, or SentencePiece library version) needed for reproducible setup. |
| Experiment Setup | Yes | The number of experts in the above three setups is 4, 3, and 6, respectively. The rank of each Lo RA expert is set to 32. During fine-tuning, we use an Adam (Kingma & Ba, 2014) optimizer with a total batch size of 64, a learning rate of 5 10 4, and an epoch of 4 on all setups. |