Summary of Variational Low-rank Adaptation Using Ivon, by Bai Cong et al.
Variational Low-Rank Adaptation Using IVON
by Bai Cong, Nico Daheim, Yuesong Shen, Daniel Cremers, Rio Yokota, Mohammad Emtiyaz Khan, Thomas Möllenhoff
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Variational learning can significantly improve the accuracy and calibration of Low-Rank Adaptation (LoRA) without increasing cost, according to a new study. Researchers replaced AdamW with the Improved Variational Online Newton (IVON) algorithm to fine-tune large language models. The IVON approach improved accuracy by 2.8% and expected calibration error by 4.6% for Llama-2, a model with 7 billion parameters. IVON outperformed other Bayesian alternatives in terms of accuracy, while requiring lower computational resources and being easier to implement. The study provides additional evidence for the effectiveness of IVON for large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can get better at understanding and responding to questions if a new algorithm is used. This algorithm is called Improved Variational Online Newton (IVON). IVON helps make sure the model is accurate and makes good predictions. It’s like a special way of tuning the model to make it work better. In this study, IVON was tested on a big language model called Llama-2. The results showed that IVON made the model 2.8% more accurate and helped it make better predictions. |
Keywords
» Artificial intelligence » Language model » Llama » Lora » Low rank adaptation
