Summary of Does Representation Matter? Exploring Intermediate Layers in Large Language Models, by Oscar Skean et al.
Does Representation Matter? Exploring Intermediate Layers in Large Language Models
by Oscar Skean, Md Rifat Arefin, Yann LeCun, Ravid Shwartz-Ziv
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 quality of intermediate representations in large language models (LLMs), including Transformers and State Space Models (SSMs). The authors find that intermediate layers often yield more informative representations for downstream tasks than the final layers. To measure representation quality, they adapt and apply a suite of metrics – prompt entropy, curvature, and augmentation-invariance – originally proposed in other contexts. Their empirical study reveals significant architectural differences, how representations evolve throughout training, and how factors like input randomness and prompt length affect each layer. The authors observe a bimodal pattern in the entropy of some intermediate layers and consider potential explanations tied to training data. This paper provides insights into the internal mechanics of LLMs and guides strategies for architectural optimization and training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at what makes good representations in big language models. They found that the middle layers often give better results than the final ones when it comes to doing tasks with the model. To figure out how good the representations are, they used some special tools – like a way to measure how confusing something is and how much it changes if you add noise to it. By studying this, they learned more about how these language models work and what makes them better or worse. This helps us understand how we can make them work even better. |
Keywords
» Artificial intelligence » Optimization » Prompt
