Summary of Towards Minimal Targeted Updates Of Language Models with Targeted Negative Training, by Lily H. Zhang and Rajesh Ranganath and Arya Tafvizi
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training
by Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi
First submitted to arxiv on: 19 Jun 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 Targeted Negative Training (TNT) method successfully updates generative language models to avoid undesirable outputs while minimizing changes to the original model behavior. By formalizing the concept of a minimal targeted update and using negative examples, TNT achieves a better trade-off between reducing unwanted behavior and maintaining model generation capabilities compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models can create impressive language, but sometimes they produce unwanted results. To fix this, researchers developed a new way to update these models so they don’t make those mistakes again. They call it Targeted Negative Training (TNT). TNT helps the model avoid bad outputs while still being good at creating new language. This is important because it lets us use language models in ways that are more responsible and useful. |
