Summary of Collapse Of Self-trained Language Models, by David Herel and Tomas Mikolov
Collapse of Self-trained Language Models
by David Herel, Tomas Mikolov
First submitted to arxiv on: 2 Apr 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 investigates the concept of self-training language models on their own outputs, mirroring human learning patterns. Specifically, it examines the potential of GPT-2 models extended through self-training, finding that prolonged self-training leads to significant degradation in performance, resulting in repetitive and collapsed token output. The study reveals practical limitations of this approach, highlighting the importance of balancing self-training with external evaluation metrics and datasets. The authors’ findings have implications for the development of advanced language models and their applications in areas such as natural language processing and machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models can learn from themselves, just like humans do. They tried extending a GPT-2 model by making it learn from its own previous work, but they found that this approach doesn’t work well after a while. The model started producing the same phrases over and over again, which isn’t useful for building new ideas. This study shows that self-training has its limits and that we need to balance it with other ways of evaluating our models. |
Keywords
» Artificial intelligence » Gpt » Machine learning » Natural language processing » Self training » Token
