Summary of Blob: Bayesian Low-rank Adaptation by Backpropagation For Large Language Models, By Yibin Wang et al.
BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
by Yibin Wang, Haizhou Shi, Ligong Han, Dimitris Metaxas, Hao Wang
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to address overconfidence in Large Language Models (LLMs) during inference is proposed, which combines Bayesian estimation with low-rank adaptation through backpropagation. This algorithm, called BLoB, continuously adjusts the mean and covariance of LLM parameters throughout fine-tuning, enabling better generalization and uncertainty estimation. Empirical results demonstrate the effectiveness of BLoB on both in-distribution and out-of-distribution data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can sometimes be too sure of themselves when doing new tasks with limited information. This problem is solved by a new way to make these models more uncertain, which works alongside a technique called Bayesian estimation. The new approach, called BLoB, helps the model learn better and make better guesses about things it doesn’t know much about. |
Keywords
» Artificial intelligence » Backpropagation » Fine tuning » Generalization » Inference » Low rank adaptation
