Summary of Unlearning Traces the Influential Training Data Of Language Models, by Masaru Isonuma and Ivan Titov
Unlearning Traces the Influential Training Data of Language Models
by Masaru Isonuma, Ivan Titov
First submitted to arxiv on: 26 Jan 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 This paper presents a method called UnTrac to identify the training datasets that influence a language model’s outputs, with the goal of minimizing harmful content generation and enhancing performance. The approach involves unlearning traces by gradient ascent, evaluating how much predictions change after unlearning. A more scalable method, UnTrac-Inv, is also proposed, which unlearns test datasets and evaluates trained models. Experiments demonstrate that these methods estimate influence more accurately than existing approaches while requiring minimal memory and no multiple checkpoints. The authors examine the influence of pretraining datasets on generating toxic, biased, and untruthful content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how training datasets affect language models. It’s like trying to figure out what makes a model “smart” or not. The researchers created two new methods: UnTrac and UnTrac-Inv. These methods help us see which training datasets are most important for making the model produce certain kinds of content, like toxic or biased messages. The good news is that these methods are very accurate without needing to retrain the model multiple times. |
Keywords
» Artificial intelligence » Language model » Pretraining
