Summary of Bora: Bayesian Hierarchical Low-rank Adaption For Multi-task Large Language Models, by Simen Eide et al.
BoRA: Bayesian Hierarchical Low-Rank Adaption for Multi-Task Large Language Models
by Simen Eide, Arnoldo Frigessi
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Bayesian Hierarchical Low-Rank Adaption (BoRA), a novel approach to fine-tuning multi-task Large Language Models (LLMs). BoRA addresses limitations in current methods by leveraging a hierarchical model that allows tasks to share information. This enables better generalization across tasks and lower perplexity. The method is shown to outperform both individual and unified model approaches, achieving improved results for diverse applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BoRA is a new way to make language models work better on many different tasks at once. Right now, people have to choose between training separate models for each task or one big model that does everything. Both options have downsides. BoRA helps by letting tasks share information with each other, so they can learn from each other even if some tasks don’t have much data. This makes it a better way to fine-tune language models and could be useful for lots of different applications. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Multi task » Perplexity
