Summary of Domba: Double Model Balancing For Access-controlled Language Models Via Minimum-bounded Aggregation, by Tom Segal et al.
DOMBA: Double Model Balancing for Access-Controlled Language Models via Minimum-Bounded Aggregation
by Tom Segal, Asaf Shabtai, Yuval Elovici
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 This paper proposes a solution for training large language models (LLMs) on sensitive data with access restrictions. The quality and quantity of training data are critical factors in determining the utility of LLMs, but many organizations have datasets that come with access restrictions due to user privileges and access control mechanisms. A straightforward approach is to train separate models for each access level, but this may result in low-utility models due to limited training data. Another approach is to train a single model on all the data while limiting exposure of unauthorized information. However, current methods are ineffective for access-controlled data where sensitive information appears frequently across many examples. The authors propose DOMBA (double model balancing), a simple approach that aggregates probability distributions from two models trained on documents with different access levels using a “min-bounded” average function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simpler terms, this paper explores ways to train language models on sensitive data while keeping the information safe. Many organizations have large datasets, but these datasets often come with restrictions due to user privileges and security measures. The authors propose a solution called DOMBA that combines two models trained on different levels of access to balance utility and security. |
Keywords
» Artificial intelligence » Probability
