Summary of Characterizing Model Collapse in Large Language Models Using Semantic Networks and Next-token Probability, by Daniele Gambetta et al.
Characterizing Model Collapse in Large Language Models Using Semantic Networks and Next-Token Probability
by Daniele Gambetta, Gizem Gezici, Fosca Giannotti, Dino Pedreschi, Alistair Knott, Luca Pappalardo
First submitted to arxiv on: 16 Oct 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 paper investigates the phenomenon of “model collapse” in generative AI models, where their performance and diversity degrade over successive generations. Researchers have explored this issue across various models and data types, but current characterizations are simplistic and lack comprehensive evaluation. The study conducts a thorough investigation using semantic networks to analyze text repetitiveness and diversity, as well as next-token probabilities to quantify the loss of diversity. The authors examine how the proportions of synthetic tokens affect the severity of model collapse and perform cross-dataset evaluations to identify domain-specific variations. By proposing metrics and strategies for assessing model collapse, the study provides new insights for developing robust generative AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative AI models are getting better at creating fake content. But what happens when they start using their own fake text to get even better? This can lead to a problem called “model collapse,” where the quality and variety of the fake text gets worse over time. Scientists have been studying this issue, but so far, it’s not very well understood. This paper takes a closer look at model collapse by analyzing three big datasets of text using special techniques like semantic networks and next-token probabilities. The researchers also looked at how the amount of fake content affects how bad the problem gets and found that different types of data can affect the severity of model collapse in different ways. By coming up with new ways to measure and understand model collapse, this study helps us make better AI systems. |
Keywords
» Artificial intelligence » Token
