Summary of Adapterswap: Continuous Training Of Llms with Data Removal and Access-control Guarantees, by William Fleshman et al.
AdapterSwap: Continuous Training of LLMs with Data Removal and Access-Control Guarantees
by William Fleshman, Aleem Khan, Marc Marone, Benjamin Van Durme
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Medium Difficulty summary: Large language models (LLMs) excel at completing knowledge-intensive tasks by drawing from static pretraining corpora. However, they struggle with evolving data requirements, such as periodic batches of new data, user-controlled subsets, or dynamically removing documents while ensuring associated knowledge remains inaccessible. To address these challenges, the authors introduce AdapterSwap, a training and inference scheme that organizes knowledge into low-rank adapters, which are composed during inference to support efficient continual learning and fine-grained control over data access and deletion. The proposed approach demonstrates its ability to adapt to changing data requirements while preserving old information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you have a super smart AI that can learn from lots of data. But what if the data keeps changing, like new information comes in or some things get deleted? This paper talks about how we can make this AI smarter by letting it adapt to these changes while keeping track of what it already knows. The authors came up with a new way called AdapterSwap that makes this possible. They tested it and found that it helps the AI learn more efficiently and keeps its old knowledge too. |
Keywords
» Artificial intelligence » Continual learning » Inference » Pretraining
