Summary of On Mesa-optimization in Autoregressively Trained Transformers: Emergence and Capability, by Chenyu Zheng et al.
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
by Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 abstract discusses autoregressively trained transformers and their ability to learn in-context through a process called mesa-optimization. Researchers have found that during pretraining, transformers learn an inner objective function that allows them to address downstream tasks. However, it is unclear whether the non-convex training dynamics will converge to this ideal mesa-optimizer. To investigate, the authors examine the non-convex dynamics of a one-layer linear causal self-attention model autoregressively trained by gradient flow. They prove that under certain conditions, an autoregressively trained transformer learns the optimal weight matrix W through gradient descent and verifies the mesa-optimization hypothesis. The study also explores the limitations of the obtained mesa-optimizer and provides simulation results to support the theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how transformers learn in-context during pretraining. Researchers have found that they learn an inner objective function that helps them with downstream tasks, but it’s not clear if this is what happens during training. The authors look at a specific type of transformer and show that under certain conditions, it learns the right weight matrix W through gradient descent, which means it implements mesa-optimization. They also talk about the limitations of this process and do some simulations to support their findings. |
Keywords
» Artificial intelligence » Gradient descent » Objective function » Optimization » Pretraining » Self attention » Transformer
