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..
Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
Authors: Xinyu Ma, Xu Chu, Zhibang Yang, Yang Lin, Xin Gao, Junfeng Zhao
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various tasks and pretrained models validate the effectiveness of our methods. In this section, we conduct extensive experiments to evaluate the effectiveness of our methods. |
| Researcher Affiliation | Academia | 1School of Computer Science, Peking University, Beijing, China 2Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, China 3Center on Frontiers of Computing Studies, Peking University, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 The fine-tuning and testing procedure of a pre-trained model with (q)GOFT. |
| Open Source Code | Yes | We implement GOFT and q GOFT for fine-tuning De BERTa V3-base (He et al., 2021) and LLa MA2-7B (Touvron et al., 2023), we also integrate our methods into the PEFT library (Mangrulkar et al., 2022) 1. 1https://github.com/ArthurLeoM/peft-givens |
| Open Datasets | Yes | Various downstream NLP tasks are applied to fine-tune the PLMs for conducting comparisons between baselines, including natural language understanding (Wang et al., 2018a, GLUE), instruction following (Hendrycks et al., 2021, MMLU) (Chiang et al., 2023, Vicuna-Eval), and question answering (Rajpurkar et al., 2016, SQu AD). We also validate the effectiveness of our method on visual tasks (Zhai et al., 2019, VTAB-1K) by fine-tuning VFMs like Vi T-B/16 (Dosovitskiy et al., 2021). |
| Dataset Splits | Yes | We present the detailed dataset statistics of GLUE benchmark (Wang et al., 2018a) in Table 6. (Table 6 shows #Dev column). SQu ADv1.1 consists of 87,599 training samples and 10,570 validation samples. |
| Hardware Specification | Yes | The experiments are conducted on a single NVIDIA-A100-80GB GPU or distributedly on a maximum of 4 NVIDIA-RTX3090-24GB GPUs. |
| Software Dependencies | No | The paper mentions software like Py Torch, Hugging Face transformers, PEFT library, and LLa MA-Factory, but does not specify their version numbers. |
| Experiment Setup | Yes | The specific tuned hyperparameters used in our experiments are presented in Table 5. |