Summary of Investigating the Impact Of Model Complexity in Large Language Models, by Jing Luo et al.
Investigating the Impact of Model Complexity in Large Language Models
by Jing Luo, Huiyuan Wang, Weiran Huang
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 explores the relationship between model complexity and fine-tuning performance in Large Language Models (LLMs) based on pre-trained fine-tuning paradigms. It proposes using Hidden Markov Models (HMMs) to model autoregressive LLMs and investigates how model complexity affects generalization capability in downstream tasks, particularly with head tuning. Theoretical analysis reveals a “double descent” phenomenon, where the risk initially increases and then decreases with rising model complexity, suggesting that the optimal balance between bias and variance occurs when the model size is zero. Experiments conducted on HMM-generated data support these findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how big language models work and how they can be improved. It uses a special type of math problem called Hidden Markov Models to understand what makes these models good or bad at doing tasks like language translation. The research shows that as you make the model bigger, it gets better at first but then starts to get worse again. This is interesting because it means that having a super big model isn’t always the best way to do a task. The results of this study can help us make even better language models in the future. |
Keywords
» Artificial intelligence » Autoregressive » Fine tuning » Generalization » Translation
