Summary of Targeted Angular Reversal Of Weights (tars) For Knowledge Removal in Large Language Models, by Harry J. Davies et al.
Targeted Angular Reversal of Weights (TARS) for Knowledge Removal in Large Language Models
by Harry J. Davies, Giorgos Iacovides, Danilo P. Mandic
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed paper introduces a novel method for removing sensitive knowledge from large language models (LLMs), specifically targeting concepts like bio-security and copyrighted works. The Targeted Angular Reversal (TARS) method aggregates information about a selected concept, refines an approximate concept vector to trigger the concept token with high probability, and then replaces relevant feedforward weight vectors in the LLM. This modular approach allows for sequential removal of concepts from the model, demonstrating reduced triggering probabilities while maintaining minimal impact on overall model capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to remove sensitive information from large language models. It creates a special method that helps keep this information private. The method is designed to work with many different languages and doesn’t ruin the model’s abilities. This means you can still use the model for lots of tasks, like understanding Wikipedia text. |
Keywords
» Artificial intelligence » Probability » Token
